chore(agents): update sort_order values for agent types to improve logical grouping

- Implemented sorting of agent types by `sort_order` in Agents page.
- Added support for category, icon, color, sort_order, typical_tasks, and collaboration_hints fields in agent creation and update actions.

.githooks/pre-commit

@@ -0,0 +1,61 @@
+#!/bin/bash
+# Pre-commit hook to enforce validation before commits on protected branches
+# Install: git config core.hooksPath .githooks
+
+set -e
+
+# Get the current branch name
+BRANCH=$(git rev-parse --abbrev-ref HEAD)
+
+# Protected branches that require validation
+PROTECTED_BRANCHES="main dev"
+
+# Check if we're on a protected branch
+is_protected() {
+    for branch in $PROTECTED_BRANCHES; do
+        if [ "$BRANCH" = "$branch" ]; then
+            return 0
+        fi
+    done
+    return 1
+}
+
+if is_protected; then
+    echo "🔒 Committing to protected branch '$BRANCH' - running validation..."
+
+    # Check if we have backend changes
+    if git diff --cached --name-only | grep -q "^backend/"; then
+        echo "📦 Backend changes detected - running make validate..."
+        cd backend
+        if ! make validate; then
+            echo ""
+            echo "❌ Backend validation failed!"
+            echo "   Please fix the issues and try again."
+            echo "   Run 'cd backend && make validate' to see errors."
+            exit 1
+        fi
+        cd ..
+        echo "✅ Backend validation passed!"
+    fi
+
+    # Check if we have frontend changes
+    if git diff --cached --name-only | grep -q "^frontend/"; then
+        echo "🎨 Frontend changes detected - running npm run validate..."
+        cd frontend
+        if ! npm run validate 2>/dev/null; then
+            echo ""
+            echo "❌ Frontend validation failed!"
+            echo "   Please fix the issues and try again."
+            echo "   Run 'cd frontend && npm run validate' to see errors."
+            exit 1
+        fi
+        cd ..
+        echo "✅ Frontend validation passed!"
+    fi
+
+    echo "🎉 All validations passed! Proceeding with commit..."
+else
+    echo "📝 Committing to feature branch '$BRANCH' - skipping validation (run manually if needed)"
+fi
+
+exit 0

Makefile

@@ -1,18 +1,34 @@
 .PHONY: help dev dev-full prod down logs logs-dev clean clean-slate drop-db reset-db push-images deploy
+.PHONY: test test-backend test-mcp test-frontend test-all test-cov test-integration validate validate-all format-all
 
 VERSION ?= latest
 REGISTRY ?= ghcr.io/cardosofelipe/pragma-stack
 
 # Default target
 help:
-	@echo "FastAPI + Next.js Full-Stack Template"
+	@echo "Syndarix - AI-Powered Software Consulting Agency"
 	@echo ""
 	@echo "Development:"
-	@echo "  make dev           - Start backend + db (frontend runs separately)"
+	@echo "  make dev           - Start backend + db + MCP servers (frontend runs separately)"
 	@echo "  make dev-full      - Start all services including frontend"
 	@echo "  make down          - Stop all services"
 	@echo "  make logs-dev      - Follow dev container logs"
 	@echo ""
+	@echo "Testing:"
+	@echo "  make test          - Run all tests (backend + MCP servers)"
+	@echo "  make test-backend  - Run backend tests only"
+	@echo "  make test-mcp      - Run MCP server tests only"
+	@echo "  make test-frontend - Run frontend tests only"
+	@echo "  make test-cov      - Run all tests with coverage reports"
+	@echo "  make test-integration - Run MCP integration tests (requires running stack)"
+	@echo ""
+	@echo "Formatting:"
+	@echo "  make format-all    - Format code in backend + MCP servers + frontend"
+	@echo ""
+	@echo "Validation:"
+	@echo "  make validate      - Validate backend + MCP servers (lint, type-check, test)"
+	@echo "  make validate-all  - Validate everything including frontend"
+	@echo ""
 	@echo "Database:"
 	@echo "  make drop-db       - Drop and recreate empty database"
 	@echo "  make reset-db      - Drop database and apply all migrations"
@@ -28,8 +44,10 @@ help:
 	@echo "  make clean-slate   - Stop containers AND delete volumes (DATA LOSS!)"
 	@echo ""
 	@echo "Subdirectory commands:"
-	@echo "  cd backend && make help   - Backend-specific commands"
-	@echo "  cd frontend && npm run    - Frontend-specific commands"
+	@echo "  cd backend && make help              - Backend-specific commands"
+	@echo "  cd mcp-servers/llm-gateway && make   - LLM Gateway commands"
+	@echo "  cd mcp-servers/knowledge-base && make - Knowledge Base commands"
+	@echo "  cd frontend && npm run               - Frontend-specific commands"
 
 # ============================================================================
 # Development
@@ -99,3 +117,91 @@ clean:
 # WARNING! THIS REMOVES CONTAINERS AND VOLUMES AS WELL - DO NOT USE THIS UNLESS YOU WANT TO START OVER WITH DATA AND ALL
 clean-slate:
 	docker compose -f docker-compose.dev.yml down -v --remove-orphans
+
+# ============================================================================
+# Testing
+# ============================================================================
+
+test: test-backend test-mcp
+	@echo ""
+	@echo "All tests passed!"
+
+test-backend:
+	@echo "Running backend tests..."
+	@cd backend && IS_TEST=True uv run pytest tests/ -v
+
+test-mcp:
+	@echo "Running MCP server tests..."
+	@echo ""
+	@echo "=== LLM Gateway ==="
+	@cd mcp-servers/llm-gateway && uv run pytest tests/ -v
+	@echo ""
+	@echo "=== Knowledge Base ==="
+	@cd mcp-servers/knowledge-base && uv run pytest tests/ -v
+
+test-frontend:
+	@echo "Running frontend tests..."
+	@cd frontend && npm test
+
+test-all: test test-frontend
+	@echo ""
+	@echo "All tests (backend + MCP + frontend) passed!"
+
+test-cov:
+	@echo "Running all tests with coverage..."
+	@echo ""
+	@echo "=== Backend Coverage ==="
+	@cd backend && IS_TEST=True uv run pytest tests/ -v --cov=app --cov-report=term-missing
+	@echo ""
+	@echo "=== LLM Gateway Coverage ==="
+	@cd mcp-servers/llm-gateway && uv run pytest tests/ -v --cov=. --cov-report=term-missing
+	@echo ""
+	@echo "=== Knowledge Base Coverage ==="
+	@cd mcp-servers/knowledge-base && uv run pytest tests/ -v --cov=. --cov-report=term-missing
+
+test-integration:
+	@echo "Running MCP integration tests..."
+	@echo "Note: Requires running stack (make dev first)"
+	@cd backend && RUN_INTEGRATION_TESTS=true IS_TEST=True uv run pytest tests/integration/ -v
+
+# ============================================================================
+# Formatting
+# ============================================================================
+
+format-all:
+	@echo "Formatting backend..."
+	@cd backend && make format
+	@echo ""
+	@echo "Formatting LLM Gateway..."
+	@cd mcp-servers/llm-gateway && make format
+	@echo ""
+	@echo "Formatting Knowledge Base..."
+	@cd mcp-servers/knowledge-base && make format
+	@echo ""
+	@echo "Formatting frontend..."
+	@cd frontend && npm run format
+	@echo ""
+	@echo "All code formatted!"
+
+# ============================================================================
+# Validation (lint + type-check + test)
+# ============================================================================
+
+validate:
+	@echo "Validating backend..."
+	@cd backend && make validate
+	@echo ""
+	@echo "Validating LLM Gateway..."
+	@cd mcp-servers/llm-gateway && make validate
+	@echo ""
+	@echo "Validating Knowledge Base..."
+	@cd mcp-servers/knowledge-base && make validate
+	@echo ""
+	@echo "All validations passed!"
+
+validate-all: validate
+	@echo ""
+	@echo "Validating frontend..."
+	@cd frontend && npm run validate
+	@echo ""
+	@echo "Full validation passed!"

backend/Makefile

@@ -1,4 +1,4 @@
-.PHONY: help lint lint-fix format format-check type-check test test-cov validate clean install-dev sync check-docker install-e2e test-e2e test-e2e-schema test-all
+.PHONY: help lint lint-fix format format-check type-check test test-cov validate clean install-dev sync check-docker install-e2e test-e2e test-e2e-schema test-all test-integration
 
 # Default target
 help:
@@ -22,6 +22,7 @@ help:
 	@echo "  make test-cov      - Run pytest with coverage report"
 	@echo "  make test-e2e      - Run E2E tests (PostgreSQL, requires Docker)"
 	@echo "  make test-e2e-schema - Run Schemathesis API schema tests"
+	@echo "  make test-integration - Run MCP integration tests (requires running stack)"
 	@echo "  make test-all      - Run all tests (unit + E2E)"
 	@echo "  make check-docker  - Check if Docker is available"
 	@echo ""
@@ -79,9 +80,18 @@ test:
 
 test-cov:
 	@echo "🧪 Running tests with coverage..."
-	@IS_TEST=True PYTHONPATH=. uv run pytest --cov=app --cov-report=term-missing --cov-report=html -n 16
+	@IS_TEST=True PYTHONPATH=. uv run pytest --cov=app --cov-report=term-missing --cov-report=html -n 20
 	@echo "📊 Coverage report generated in htmlcov/index.html"
 
+# ============================================================================
+# Integration Testing (requires running stack: make dev)
+# ============================================================================
+
+test-integration:
+	@echo "🧪 Running MCP integration tests..."
+	@echo "Note: Requires running stack (make dev from project root)"
+	@RUN_INTEGRATION_TESTS=true IS_TEST=True PYTHONPATH=. uv run pytest tests/integration/ -v
+
 # ============================================================================
 # E2E Testing (requires Docker)
 # ============================================================================

backend/app/alembic/versions/0005_add_memory_system_tables.py

@@ -0,0 +1,512 @@
+"""Add Agent Memory System tables
+
+Revision ID: 0005
+Revises: 0004
+Create Date: 2025-01-05
+
+This migration creates the Agent Memory System tables:
+- working_memory: Key-value storage with TTL for active sessions
+- episodes: Experiential memories from task executions
+- facts: Semantic knowledge triples with confidence scores
+- procedures: Learned skills and procedures
+- memory_consolidation_log: Tracks consolidation jobs
+
+See Issue #88: Database Schema & Storage Layer
+"""
+
+from collections.abc import Sequence
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision: str = "0005"
+down_revision: str | None = "0004"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def upgrade() -> None:
+    """Create Agent Memory System tables."""
+
+    # =========================================================================
+    # Create ENUM types for memory system
+    # =========================================================================
+
+    # Scope type enum
+    scope_type_enum = postgresql.ENUM(
+        "global",
+        "project",
+        "agent_type",
+        "agent_instance",
+        "session",
+        name="scope_type",
+        create_type=False,
+    )
+    scope_type_enum.create(op.get_bind(), checkfirst=True)
+
+    # Episode outcome enum
+    episode_outcome_enum = postgresql.ENUM(
+        "success",
+        "failure",
+        "partial",
+        name="episode_outcome",
+        create_type=False,
+    )
+    episode_outcome_enum.create(op.get_bind(), checkfirst=True)
+
+    # Consolidation type enum
+    consolidation_type_enum = postgresql.ENUM(
+        "working_to_episodic",
+        "episodic_to_semantic",
+        "episodic_to_procedural",
+        "pruning",
+        name="consolidation_type",
+        create_type=False,
+    )
+    consolidation_type_enum.create(op.get_bind(), checkfirst=True)
+
+    # Consolidation status enum
+    consolidation_status_enum = postgresql.ENUM(
+        "pending",
+        "running",
+        "completed",
+        "failed",
+        name="consolidation_status",
+        create_type=False,
+    )
+    consolidation_status_enum.create(op.get_bind(), checkfirst=True)
+
+    # =========================================================================
+    # Create working_memory table
+    # Key-value storage with TTL for active sessions
+    # =========================================================================
+    op.create_table(
+        "working_memory",
+        sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column(
+            "scope_type",
+            scope_type_enum,
+            nullable=False,
+        ),
+        sa.Column("scope_id", sa.String(255), nullable=False),
+        sa.Column("key", sa.String(255), nullable=False),
+        sa.Column("value", postgresql.JSONB(astext_type=sa.Text()), nullable=False),
+        sa.Column("expires_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.PrimaryKeyConstraint("id"),
+    )
+
+    # Working memory indexes
+    op.create_index(
+        "ix_working_memory_scope_type",
+        "working_memory",
+        ["scope_type"],
+    )
+    op.create_index(
+        "ix_working_memory_scope_id",
+        "working_memory",
+        ["scope_id"],
+    )
+    op.create_index(
+        "ix_working_memory_scope_key",
+        "working_memory",
+        ["scope_type", "scope_id", "key"],
+        unique=True,
+    )
+    op.create_index(
+        "ix_working_memory_expires",
+        "working_memory",
+        ["expires_at"],
+    )
+    op.create_index(
+        "ix_working_memory_scope_list",
+        "working_memory",
+        ["scope_type", "scope_id"],
+    )
+
+    # =========================================================================
+    # Create episodes table
+    # Experiential memories from task executions
+    # =========================================================================
+    op.create_table(
+        "episodes",
+        sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("project_id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("agent_instance_id", postgresql.UUID(as_uuid=True), nullable=True),
+        sa.Column("agent_type_id", postgresql.UUID(as_uuid=True), nullable=True),
+        sa.Column("session_id", sa.String(255), nullable=False),
+        sa.Column("task_type", sa.String(100), nullable=False),
+        sa.Column("task_description", sa.Text(), nullable=False),
+        sa.Column(
+            "actions",
+            postgresql.JSONB(astext_type=sa.Text()),
+            nullable=False,
+            server_default="[]",
+        ),
+        sa.Column("context_summary", sa.Text(), nullable=False),
+        sa.Column(
+            "outcome",
+            episode_outcome_enum,
+            nullable=False,
+        ),
+        sa.Column("outcome_details", sa.Text(), nullable=True),
+        sa.Column("duration_seconds", sa.Float(), nullable=False, server_default="0.0"),
+        sa.Column("tokens_used", sa.BigInteger(), nullable=False, server_default="0"),
+        sa.Column(
+            "lessons_learned",
+            postgresql.JSONB(astext_type=sa.Text()),
+            nullable=False,
+            server_default="[]",
+        ),
+        sa.Column("importance_score", sa.Float(), nullable=False, server_default="0.5"),
+        # Vector embedding - using TEXT as fallback, will be VECTOR(1536) when pgvector is available
+        sa.Column("embedding", sa.Text(), nullable=True),
+        sa.Column("occurred_at", sa.DateTime(timezone=True), nullable=False),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.PrimaryKeyConstraint("id"),
+        sa.ForeignKeyConstraint(
+            ["project_id"],
+            ["projects.id"],
+            name="fk_episodes_project",
+            ondelete="CASCADE",
+        ),
+        sa.ForeignKeyConstraint(
+            ["agent_instance_id"],
+            ["agent_instances.id"],
+            name="fk_episodes_agent_instance",
+            ondelete="SET NULL",
+        ),
+        sa.ForeignKeyConstraint(
+            ["agent_type_id"],
+            ["agent_types.id"],
+            name="fk_episodes_agent_type",
+            ondelete="SET NULL",
+        ),
+    )
+
+    # Episode indexes
+    op.create_index("ix_episodes_project_id", "episodes", ["project_id"])
+    op.create_index("ix_episodes_agent_instance_id", "episodes", ["agent_instance_id"])
+    op.create_index("ix_episodes_agent_type_id", "episodes", ["agent_type_id"])
+    op.create_index("ix_episodes_session_id", "episodes", ["session_id"])
+    op.create_index("ix_episodes_task_type", "episodes", ["task_type"])
+    op.create_index("ix_episodes_outcome", "episodes", ["outcome"])
+    op.create_index("ix_episodes_importance_score", "episodes", ["importance_score"])
+    op.create_index("ix_episodes_occurred_at", "episodes", ["occurred_at"])
+    op.create_index("ix_episodes_project_task", "episodes", ["project_id", "task_type"])
+    op.create_index(
+        "ix_episodes_project_outcome", "episodes", ["project_id", "outcome"]
+    )
+    op.create_index(
+        "ix_episodes_agent_task", "episodes", ["agent_instance_id", "task_type"]
+    )
+    op.create_index(
+        "ix_episodes_project_time", "episodes", ["project_id", "occurred_at"]
+    )
+    op.create_index(
+        "ix_episodes_importance_time",
+        "episodes",
+        ["importance_score", "occurred_at"],
+    )
+
+    # =========================================================================
+    # Create facts table
+    # Semantic knowledge triples with confidence scores
+    # =========================================================================
+    op.create_table(
+        "facts",
+        sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column(
+            "project_id", postgresql.UUID(as_uuid=True), nullable=True
+        ),  # NULL for global facts
+        sa.Column("subject", sa.String(500), nullable=False),
+        sa.Column("predicate", sa.String(255), nullable=False),
+        sa.Column("object", sa.Text(), nullable=False),
+        sa.Column("confidence", sa.Float(), nullable=False, server_default="0.8"),
+        # Source episode IDs stored as JSON array of UUID strings for cross-db compatibility
+        sa.Column(
+            "source_episode_ids",
+            postgresql.JSONB(astext_type=sa.Text()),
+            nullable=False,
+            server_default="[]",
+        ),
+        sa.Column("first_learned", sa.DateTime(timezone=True), nullable=False),
+        sa.Column("last_reinforced", sa.DateTime(timezone=True), nullable=False),
+        sa.Column(
+            "reinforcement_count", sa.Integer(), nullable=False, server_default="1"
+        ),
+        # Vector embedding
+        sa.Column("embedding", sa.Text(), nullable=True),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.PrimaryKeyConstraint("id"),
+        sa.ForeignKeyConstraint(
+            ["project_id"],
+            ["projects.id"],
+            name="fk_facts_project",
+            ondelete="CASCADE",
+        ),
+    )
+
+    # Fact indexes
+    op.create_index("ix_facts_project_id", "facts", ["project_id"])
+    op.create_index("ix_facts_subject", "facts", ["subject"])
+    op.create_index("ix_facts_predicate", "facts", ["predicate"])
+    op.create_index("ix_facts_confidence", "facts", ["confidence"])
+    op.create_index("ix_facts_subject_predicate", "facts", ["subject", "predicate"])
+    op.create_index("ix_facts_project_subject", "facts", ["project_id", "subject"])
+    op.create_index(
+        "ix_facts_confidence_time", "facts", ["confidence", "last_reinforced"]
+    )
+    # Unique constraint for triples within project scope
+    op.create_index(
+        "ix_facts_unique_triple",
+        "facts",
+        ["project_id", "subject", "predicate", "object"],
+        unique=True,
+        postgresql_where=sa.text("project_id IS NOT NULL"),
+    )
+    # Unique constraint for global facts (project_id IS NULL)
+    op.create_index(
+        "ix_facts_unique_triple_global",
+        "facts",
+        ["subject", "predicate", "object"],
+        unique=True,
+        postgresql_where=sa.text("project_id IS NULL"),
+    )
+
+    # =========================================================================
+    # Create procedures table
+    # Learned skills and procedures
+    # =========================================================================
+    op.create_table(
+        "procedures",
+        sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column("project_id", postgresql.UUID(as_uuid=True), nullable=True),
+        sa.Column("agent_type_id", postgresql.UUID(as_uuid=True), nullable=True),
+        sa.Column("name", sa.String(255), nullable=False),
+        sa.Column("trigger_pattern", sa.Text(), nullable=False),
+        sa.Column(
+            "steps",
+            postgresql.JSONB(astext_type=sa.Text()),
+            nullable=False,
+            server_default="[]",
+        ),
+        sa.Column("success_count", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("failure_count", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("last_used", sa.DateTime(timezone=True), nullable=True),
+        # Vector embedding
+        sa.Column("embedding", sa.Text(), nullable=True),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.PrimaryKeyConstraint("id"),
+        sa.ForeignKeyConstraint(
+            ["project_id"],
+            ["projects.id"],
+            name="fk_procedures_project",
+            ondelete="CASCADE",
+        ),
+        sa.ForeignKeyConstraint(
+            ["agent_type_id"],
+            ["agent_types.id"],
+            name="fk_procedures_agent_type",
+            ondelete="SET NULL",
+        ),
+    )
+
+    # Procedure indexes
+    op.create_index("ix_procedures_project_id", "procedures", ["project_id"])
+    op.create_index("ix_procedures_agent_type_id", "procedures", ["agent_type_id"])
+    op.create_index("ix_procedures_name", "procedures", ["name"])
+    op.create_index("ix_procedures_last_used", "procedures", ["last_used"])
+    op.create_index(
+        "ix_procedures_unique_name",
+        "procedures",
+        ["project_id", "agent_type_id", "name"],
+        unique=True,
+    )
+    op.create_index("ix_procedures_project_name", "procedures", ["project_id", "name"])
+    # Note: agent_type_id already indexed via ix_procedures_agent_type_id (line 354)
+    op.create_index(
+        "ix_procedures_success_rate",
+        "procedures",
+        ["success_count", "failure_count"],
+    )
+
+    # =========================================================================
+    # Add check constraints for data integrity
+    # =========================================================================
+
+    # Episode constraints
+    op.create_check_constraint(
+        "ck_episodes_importance_range",
+        "episodes",
+        "importance_score >= 0.0 AND importance_score <= 1.0",
+    )
+    op.create_check_constraint(
+        "ck_episodes_duration_positive",
+        "episodes",
+        "duration_seconds >= 0.0",
+    )
+    op.create_check_constraint(
+        "ck_episodes_tokens_positive",
+        "episodes",
+        "tokens_used >= 0",
+    )
+
+    # Fact constraints
+    op.create_check_constraint(
+        "ck_facts_confidence_range",
+        "facts",
+        "confidence >= 0.0 AND confidence <= 1.0",
+    )
+    op.create_check_constraint(
+        "ck_facts_reinforcement_positive",
+        "facts",
+        "reinforcement_count >= 1",
+    )
+
+    # Procedure constraints
+    op.create_check_constraint(
+        "ck_procedures_success_positive",
+        "procedures",
+        "success_count >= 0",
+    )
+    op.create_check_constraint(
+        "ck_procedures_failure_positive",
+        "procedures",
+        "failure_count >= 0",
+    )
+
+    # =========================================================================
+    # Create memory_consolidation_log table
+    # Tracks consolidation jobs
+    # =========================================================================
+    op.create_table(
+        "memory_consolidation_log",
+        sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False),
+        sa.Column(
+            "consolidation_type",
+            consolidation_type_enum,
+            nullable=False,
+        ),
+        sa.Column("source_count", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("result_count", sa.Integer(), nullable=False, server_default="0"),
+        sa.Column("started_at", sa.DateTime(timezone=True), nullable=False),
+        sa.Column("completed_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column(
+            "status",
+            consolidation_status_enum,
+            nullable=False,
+            server_default="pending",
+        ),
+        sa.Column("error", sa.Text(), nullable=True),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.text("now()"),
+        ),
+        sa.PrimaryKeyConstraint("id"),
+    )
+
+    # Consolidation log indexes
+    op.create_index(
+        "ix_consolidation_type",
+        "memory_consolidation_log",
+        ["consolidation_type"],
+    )
+    op.create_index(
+        "ix_consolidation_status",
+        "memory_consolidation_log",
+        ["status"],
+    )
+    op.create_index(
+        "ix_consolidation_type_status",
+        "memory_consolidation_log",
+        ["consolidation_type", "status"],
+    )
+    op.create_index(
+        "ix_consolidation_started",
+        "memory_consolidation_log",
+        ["started_at"],
+    )
+
+
+def downgrade() -> None:
+    """Drop Agent Memory System tables."""
+
+    # Drop check constraints first
+    op.drop_constraint("ck_procedures_failure_positive", "procedures", type_="check")
+    op.drop_constraint("ck_procedures_success_positive", "procedures", type_="check")
+    op.drop_constraint("ck_facts_reinforcement_positive", "facts", type_="check")
+    op.drop_constraint("ck_facts_confidence_range", "facts", type_="check")
+    op.drop_constraint("ck_episodes_tokens_positive", "episodes", type_="check")
+    op.drop_constraint("ck_episodes_duration_positive", "episodes", type_="check")
+    op.drop_constraint("ck_episodes_importance_range", "episodes", type_="check")
+
+    # Drop unique indexes for global facts
+    op.drop_index("ix_facts_unique_triple_global", "facts")
+
+    # Drop tables in reverse order (dependencies first)
+    op.drop_table("memory_consolidation_log")
+    op.drop_table("procedures")
+    op.drop_table("facts")
+    op.drop_table("episodes")
+    op.drop_table("working_memory")
+
+    # Drop ENUM types
+    op.execute("DROP TYPE IF EXISTS consolidation_status")
+    op.execute("DROP TYPE IF EXISTS consolidation_type")
+    op.execute("DROP TYPE IF EXISTS episode_outcome")
+    op.execute("DROP TYPE IF EXISTS scope_type")

backend/app/alembic/versions/0006_add_abandoned_outcome.py

@@ -0,0 +1,52 @@
+"""Add ABANDONED to episode_outcome enum
+
+Revision ID: 0006
+Revises: 0005
+Create Date: 2025-01-06
+
+This migration adds the 'abandoned' value to the episode_outcome enum type.
+This allows episodes to track when a task was abandoned (not completed,
+but not necessarily a failure either - e.g., user cancelled, session timeout).
+"""
+
+from collections.abc import Sequence
+
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "0006"
+down_revision: str | None = "0005"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def upgrade() -> None:
+    """Add 'abandoned' value to episode_outcome enum."""
+    # PostgreSQL ALTER TYPE ADD VALUE is safe and non-blocking
+    op.execute("ALTER TYPE episode_outcome ADD VALUE IF NOT EXISTS 'abandoned'")
+
+
+def downgrade() -> None:
+    """Remove 'abandoned' from episode_outcome enum.
+
+    Note: PostgreSQL doesn't support removing values from enums directly.
+    This downgrade converts any 'abandoned' episodes to 'failure' and
+    recreates the enum without 'abandoned'.
+    """
+    # Convert any abandoned episodes to failure first
+    op.execute("""
+        UPDATE episodes
+        SET outcome = 'failure'
+        WHERE outcome = 'abandoned'
+    """)
+
+    # Recreate the enum without abandoned
+    # This is complex in PostgreSQL - requires creating new type, updating columns, dropping old
+    op.execute("ALTER TYPE episode_outcome RENAME TO episode_outcome_old")
+    op.execute("CREATE TYPE episode_outcome AS ENUM ('success', 'failure', 'partial')")
+    op.execute("""
+        ALTER TABLE episodes
+        ALTER COLUMN outcome TYPE episode_outcome
+        USING outcome::text::episode_outcome
+    """)
+    op.execute("DROP TYPE episode_outcome_old")

backend/app/alembic/versions/0007_add_agent_type_category_fields.py

@@ -0,0 +1,90 @@
+"""Add category and display fields to agent_types table
+
+Revision ID: 0007
+Revises: 0006
+Create Date: 2026-01-06
+
+This migration adds:
+- category: String(50) for grouping agents by role type
+- icon: String(50) for Lucide icon identifier
+- color: String(7) for hex color code
+- sort_order: Integer for display ordering within categories
+- typical_tasks: JSONB list of tasks this agent excels at
+- collaboration_hints: JSONB list of agent slugs that work well together
+"""
+
+from collections.abc import Sequence
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision: str = "0007"
+down_revision: str | None = "0006"
+branch_labels: str | Sequence[str] | None = None
+depends_on: str | Sequence[str] | None = None
+
+
+def upgrade() -> None:
+    """Add category and display fields to agent_types table."""
+    # Add new columns
+    op.add_column(
+        "agent_types",
+        sa.Column("category", sa.String(length=50), nullable=True),
+    )
+    op.add_column(
+        "agent_types",
+        sa.Column("icon", sa.String(length=50), nullable=True, server_default="bot"),
+    )
+    op.add_column(
+        "agent_types",
+        sa.Column(
+            "color", sa.String(length=7), nullable=True, server_default="#3B82F6"
+        ),
+    )
+    op.add_column(
+        "agent_types",
+        sa.Column("sort_order", sa.Integer(), nullable=False, server_default="0"),
+    )
+    op.add_column(
+        "agent_types",
+        sa.Column(
+            "typical_tasks",
+            postgresql.JSONB(astext_type=sa.Text()),
+            nullable=False,
+            server_default="[]",
+        ),
+    )
+    op.add_column(
+        "agent_types",
+        sa.Column(
+            "collaboration_hints",
+            postgresql.JSONB(astext_type=sa.Text()),
+            nullable=False,
+            server_default="[]",
+        ),
+    )
+
+    # Add indexes for category and sort_order
+    op.create_index("ix_agent_types_category", "agent_types", ["category"])
+    op.create_index("ix_agent_types_sort_order", "agent_types", ["sort_order"])
+    op.create_index(
+        "ix_agent_types_category_sort", "agent_types", ["category", "sort_order"]
+    )
+
+
+def downgrade() -> None:
+    """Remove category and display fields from agent_types table."""
+    # Drop indexes
+    op.drop_index("ix_agent_types_category_sort", table_name="agent_types")
+    op.drop_index("ix_agent_types_sort_order", table_name="agent_types")
+    op.drop_index("ix_agent_types_category", table_name="agent_types")
+
+    # Drop columns
+    op.drop_column("agent_types", "collaboration_hints")
+    op.drop_column("agent_types", "typical_tasks")
+    op.drop_column("agent_types", "sort_order")
+    op.drop_column("agent_types", "color")
+    op.drop_column("agent_types", "icon")
+    op.drop_column("agent_types", "category")

backend/app/api/main.py

@@ -5,6 +5,7 @@ from app.api.routes import (
     agent_types,
     agents,
     auth,
+    context,
     events,
     issues,
     mcp,
@@ -35,6 +36,9 @@ api_router.include_router(events.router, tags=["Events"])
 # MCP (Model Context Protocol) router
 api_router.include_router(mcp.router, prefix="/mcp", tags=["MCP"])
 
+# Context Management Engine router
+api_router.include_router(context.router, prefix="/context", tags=["Context"])
+
 # Syndarix domain routers
 api_router.include_router(projects.router, prefix="/projects", tags=["Projects"])
 api_router.include_router(

backend/app/api/routes/agent_types.py

@@ -81,6 +81,13 @@ def _build_agent_type_response(
         mcp_servers=agent_type.mcp_servers,
         tool_permissions=agent_type.tool_permissions,
         is_active=agent_type.is_active,
+        # Category and display fields
+        category=agent_type.category,
+        icon=agent_type.icon,
+        color=agent_type.color,
+        sort_order=agent_type.sort_order,
+        typical_tasks=agent_type.typical_tasks or [],
+        collaboration_hints=agent_type.collaboration_hints or [],
         created_at=agent_type.created_at,
         updated_at=agent_type.updated_at,
         instance_count=instance_count,
@@ -300,6 +307,7 @@ async def list_agent_types(
     request: Request,
     pagination: PaginationParams = Depends(),
     is_active: bool = Query(True, description="Filter by active status"),
+    category: str | None = Query(None, description="Filter by category"),
     search: str | None = Query(None, description="Search by name, slug, description"),
     current_user: User = Depends(get_current_user),
     db: AsyncSession = Depends(get_db),
@@ -314,6 +322,7 @@ async def list_agent_types(
         request: FastAPI request object
         pagination: Pagination parameters (page, limit)
         is_active: Filter by active status (default: True)
+        category: Filter by category (e.g., "development", "design")
         search: Optional search term for name, slug, description
         current_user: Authenticated user
         db: Database session
@@ -328,6 +337,7 @@ async def list_agent_types(
             skip=pagination.offset,
             limit=pagination.limit,
             is_active=is_active,
+            category=category,
             search=search,
         )
 
@@ -354,6 +364,51 @@ async def list_agent_types(
         raise
 
 
+@router.get(
+    "/grouped",
+    response_model=dict[str, list[AgentTypeResponse]],
+    summary="List Agent Types Grouped by Category",
+    description="Get all agent types organized by category",
+    operation_id="list_agent_types_grouped",
+)
+@limiter.limit(f"{60 * RATE_MULTIPLIER}/minute")
+async def list_agent_types_grouped(
+    request: Request,
+    is_active: bool = Query(True, description="Filter by active status"),
+    current_user: User = Depends(get_current_user),
+    db: AsyncSession = Depends(get_db),
+) -> Any:
+    """
+    Get agent types grouped by category.
+
+    Returns a dictionary where keys are category names and values
+    are lists of agent types, sorted by sort_order within each category.
+
+    Args:
+        request: FastAPI request object
+        is_active: Filter by active status (default: True)
+        current_user: Authenticated user
+        db: Database session
+
+    Returns:
+        Dictionary mapping category to list of agent types
+    """
+    try:
+        grouped = await agent_type_crud.get_grouped_by_category(db, is_active=is_active)
+
+        # Transform to response objects
+        result: dict[str, list[AgentTypeResponse]] = {}
+        for category, types in grouped.items():
+            result[category] = [
+                _build_agent_type_response(t, instance_count=0) for t in types
+            ]
+
+        return result
+    except Exception as e:
+        logger.error(f"Error getting grouped agent types: {e!s}", exc_info=True)
+        raise
+
+
 @router.get(
     "/{agent_type_id}",
     response_model=AgentTypeResponse,

backend/app/api/routes/context.py

@@ -0,0 +1,411 @@
+"""
+Context Management API Endpoints.
+
+Provides REST endpoints for context assembly and optimization
+for LLM requests using the ContextEngine.
+"""
+
+import logging
+from typing import Annotated, Any
+
+from fastapi import APIRouter, Depends, HTTPException, Query, status
+from pydantic import BaseModel, Field
+
+from app.api.dependencies.permissions import require_superuser
+from app.models.user import User
+from app.services.context import (
+    AssemblyTimeoutError,
+    BudgetExceededError,
+    ContextEngine,
+    ContextSettings,
+    create_context_engine,
+    get_context_settings,
+)
+from app.services.mcp import MCPClientManager, get_mcp_client
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter()
+
+
+# ============================================================================
+# Singleton Engine Management
+# ============================================================================
+
+_context_engine: ContextEngine | None = None
+
+
+def _get_or_create_engine(
+    mcp: MCPClientManager,
+    settings: ContextSettings | None = None,
+) -> ContextEngine:
+    """Get or create the singleton ContextEngine."""
+    global _context_engine
+    if _context_engine is None:
+        _context_engine = create_context_engine(
+            mcp_manager=mcp,
+            redis=None,  # Optional: add Redis caching later
+            settings=settings or get_context_settings(),
+        )
+        logger.info("ContextEngine initialized")
+    else:
+        # Ensure MCP manager is up to date
+        _context_engine.set_mcp_manager(mcp)
+    return _context_engine
+
+
+async def get_context_engine(
+    mcp: MCPClientManager = Depends(get_mcp_client),
+) -> ContextEngine:
+    """FastAPI dependency to get the ContextEngine."""
+    return _get_or_create_engine(mcp)
+
+
+# ============================================================================
+# Request/Response Schemas
+# ============================================================================
+
+
+class ConversationTurn(BaseModel):
+    """A single conversation turn."""
+
+    role: str = Field(..., description="Role: 'user' or 'assistant'")
+    content: str = Field(..., description="Message content")
+
+
+class ToolResult(BaseModel):
+    """A tool execution result."""
+
+    tool_name: str = Field(..., description="Name of the tool")
+    content: str | dict[str, Any] = Field(..., description="Tool result content")
+    status: str = Field(default="success", description="Execution status")
+
+
+class AssembleContextRequest(BaseModel):
+    """Request to assemble context for an LLM request."""
+
+    project_id: str = Field(..., description="Project identifier")
+    agent_id: str = Field(..., description="Agent identifier")
+    query: str = Field(..., description="User's query or current request")
+    model: str = Field(
+        default="claude-3-sonnet",
+        description="Target model name",
+    )
+    max_tokens: int | None = Field(
+        None,
+        description="Maximum context tokens (uses model default if None)",
+    )
+    system_prompt: str | None = Field(
+        None,
+        description="System prompt/instructions",
+    )
+    task_description: str | None = Field(
+        None,
+        description="Current task description",
+    )
+    knowledge_query: str | None = Field(
+        None,
+        description="Query for knowledge base search",
+    )
+    knowledge_limit: int = Field(
+        default=10,
+        ge=1,
+        le=50,
+        description="Max number of knowledge results",
+    )
+    conversation_history: list[ConversationTurn] | None = Field(
+        None,
+        description="Previous conversation turns",
+    )
+    tool_results: list[ToolResult] | None = Field(
+        None,
+        description="Tool execution results to include",
+    )
+    compress: bool = Field(
+        default=True,
+        description="Whether to apply compression",
+    )
+    use_cache: bool = Field(
+        default=True,
+        description="Whether to use caching",
+    )
+
+
+class AssembledContextResponse(BaseModel):
+    """Response containing assembled context."""
+
+    content: str = Field(..., description="Assembled context content")
+    total_tokens: int = Field(..., description="Total token count")
+    context_count: int = Field(..., description="Number of context items included")
+    compressed: bool = Field(..., description="Whether compression was applied")
+    budget_used_percent: float = Field(
+        ...,
+        description="Percentage of token budget used",
+    )
+    metadata: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional metadata",
+    )
+
+
+class TokenCountRequest(BaseModel):
+    """Request to count tokens in content."""
+
+    content: str = Field(..., description="Content to count tokens in")
+    model: str | None = Field(
+        None,
+        description="Model for model-specific tokenization",
+    )
+
+
+class TokenCountResponse(BaseModel):
+    """Response containing token count."""
+
+    token_count: int = Field(..., description="Number of tokens")
+    model: str | None = Field(None, description="Model used for counting")
+
+
+class BudgetInfoResponse(BaseModel):
+    """Response containing budget information for a model."""
+
+    model: str = Field(..., description="Model name")
+    total_tokens: int = Field(..., description="Total token budget")
+    system_tokens: int = Field(..., description="Tokens reserved for system")
+    knowledge_tokens: int = Field(..., description="Tokens for knowledge")
+    conversation_tokens: int = Field(..., description="Tokens for conversation")
+    tool_tokens: int = Field(..., description="Tokens for tool results")
+    response_reserve: int = Field(..., description="Tokens reserved for response")
+
+
+class ContextEngineStatsResponse(BaseModel):
+    """Response containing engine statistics."""
+
+    cache: dict[str, Any] = Field(..., description="Cache statistics")
+    settings: dict[str, Any] = Field(..., description="Current settings")
+
+
+class HealthResponse(BaseModel):
+    """Health check response."""
+
+    status: str = Field(..., description="Health status")
+    mcp_connected: bool = Field(..., description="Whether MCP is connected")
+    cache_enabled: bool = Field(..., description="Whether caching is enabled")
+
+
+# ============================================================================
+# Endpoints
+# ============================================================================
+
+
+@router.get(
+    "/health",
+    response_model=HealthResponse,
+    summary="Context Engine Health",
+    description="Check health status of the context engine.",
+)
+async def health_check(
+    engine: ContextEngine = Depends(get_context_engine),
+) -> HealthResponse:
+    """Check context engine health."""
+    stats = await engine.get_stats()
+    return HealthResponse(
+        status="healthy",
+        mcp_connected=engine._mcp is not None,
+        cache_enabled=stats.get("settings", {}).get("cache_enabled", False),
+    )
+
+
+@router.post(
+    "/assemble",
+    response_model=AssembledContextResponse,
+    summary="Assemble Context",
+    description="Assemble optimized context for an LLM request.",
+)
+async def assemble_context(
+    request: AssembleContextRequest,
+    current_user: User = Depends(require_superuser),
+    engine: ContextEngine = Depends(get_context_engine),
+) -> AssembledContextResponse:
+    """
+    Assemble optimized context for an LLM request.
+
+    This endpoint gathers context from various sources, scores and ranks them,
+    compresses if needed, and formats for the target model.
+    """
+    logger.info(
+        "Context assembly for project=%s agent=%s by user=%s",
+        request.project_id,
+        request.agent_id,
+        current_user.id,
+    )
+
+    # Convert conversation history to dict format
+    conversation_history = None
+    if request.conversation_history:
+        conversation_history = [
+            {"role": turn.role, "content": turn.content}
+            for turn in request.conversation_history
+        ]
+
+    # Convert tool results to dict format
+    tool_results = None
+    if request.tool_results:
+        tool_results = [
+            {
+                "tool_name": tr.tool_name,
+                "content": tr.content,
+                "status": tr.status,
+            }
+            for tr in request.tool_results
+        ]
+
+    try:
+        result = await engine.assemble_context(
+            project_id=request.project_id,
+            agent_id=request.agent_id,
+            query=request.query,
+            model=request.model,
+            max_tokens=request.max_tokens,
+            system_prompt=request.system_prompt,
+            task_description=request.task_description,
+            knowledge_query=request.knowledge_query,
+            knowledge_limit=request.knowledge_limit,
+            conversation_history=conversation_history,
+            tool_results=tool_results,
+            compress=request.compress,
+            use_cache=request.use_cache,
+        )
+
+        # Calculate budget usage percentage
+        budget = await engine.get_budget_for_model(request.model, request.max_tokens)
+        budget_used_percent = (result.total_tokens / budget.total) * 100
+
+        # Check if compression was applied (from metadata if available)
+        was_compressed = result.metadata.get("compressed_contexts", 0) > 0
+
+        return AssembledContextResponse(
+            content=result.content,
+            total_tokens=result.total_tokens,
+            context_count=result.context_count,
+            compressed=was_compressed,
+            budget_used_percent=round(budget_used_percent, 2),
+            metadata={
+                "model": request.model,
+                "query": request.query,
+                "knowledge_included": bool(request.knowledge_query),
+                "conversation_turns": len(request.conversation_history or []),
+                "excluded_count": result.excluded_count,
+                "assembly_time_ms": result.assembly_time_ms,
+            },
+        )
+
+    except AssemblyTimeoutError as e:
+        raise HTTPException(
+            status_code=status.HTTP_504_GATEWAY_TIMEOUT,
+            detail=f"Context assembly timed out: {e}",
+        ) from e
+    except BudgetExceededError as e:
+        raise HTTPException(
+            status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
+            detail=f"Token budget exceeded: {e}",
+        ) from e
+    except Exception as e:
+        logger.exception("Context assembly failed")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Context assembly failed: {e}",
+        ) from e
+
+
+@router.post(
+    "/count-tokens",
+    response_model=TokenCountResponse,
+    summary="Count Tokens",
+    description="Count tokens in content using the LLM Gateway.",
+)
+async def count_tokens(
+    request: TokenCountRequest,
+    engine: ContextEngine = Depends(get_context_engine),
+) -> TokenCountResponse:
+    """Count tokens in content."""
+    try:
+        count = await engine.count_tokens(
+            content=request.content,
+            model=request.model,
+        )
+        return TokenCountResponse(
+            token_count=count,
+            model=request.model,
+        )
+    except Exception as e:
+        logger.warning(f"Token counting failed: {e}")
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"Token counting failed: {e}",
+        ) from e
+
+
+@router.get(
+    "/budget/{model}",
+    response_model=BudgetInfoResponse,
+    summary="Get Token Budget",
+    description="Get token budget allocation for a specific model.",
+)
+async def get_budget(
+    model: str,
+    max_tokens: Annotated[int | None, Query(description="Custom max tokens")] = None,
+    engine: ContextEngine = Depends(get_context_engine),
+) -> BudgetInfoResponse:
+    """Get token budget information for a model."""
+    budget = await engine.get_budget_for_model(model, max_tokens)
+    return BudgetInfoResponse(
+        model=model,
+        total_tokens=budget.total,
+        system_tokens=budget.system,
+        knowledge_tokens=budget.knowledge,
+        conversation_tokens=budget.conversation,
+        tool_tokens=budget.tools,
+        response_reserve=budget.response_reserve,
+    )
+
+
+@router.get(
+    "/stats",
+    response_model=ContextEngineStatsResponse,
+    summary="Engine Statistics",
+    description="Get context engine statistics and configuration.",
+)
+async def get_stats(
+    current_user: User = Depends(require_superuser),
+    engine: ContextEngine = Depends(get_context_engine),
+) -> ContextEngineStatsResponse:
+    """Get engine statistics."""
+    stats = await engine.get_stats()
+    return ContextEngineStatsResponse(
+        cache=stats.get("cache", {}),
+        settings=stats.get("settings", {}),
+    )
+
+
+@router.post(
+    "/cache/invalidate",
+    status_code=status.HTTP_204_NO_CONTENT,
+    summary="Invalidate Cache (Admin Only)",
+    description="Invalidate context cache entries.",
+)
+async def invalidate_cache(
+    project_id: Annotated[
+        str | None, Query(description="Project to invalidate")
+    ] = None,
+    pattern: Annotated[str | None, Query(description="Pattern to match")] = None,
+    current_user: User = Depends(require_superuser),
+    engine: ContextEngine = Depends(get_context_engine),
+) -> None:
+    """Invalidate cache entries."""
+    logger.info(
+        "Cache invalidation by user %s: project=%s pattern=%s",
+        current_user.id,
+        project_id,
+        pattern,
+    )
+    await engine.invalidate_cache(project_id=project_id, pattern=pattern)

backend/app/core/demo_data.json

@@ -1,366 +0,0 @@
-{
-    "organizations": [
-        {
-            "name": "Acme Corp",
-            "slug": "acme-corp",
-            "description": "A leading provider of coyote-catching equipment."
-        },
-        {
-            "name": "Globex Corporation",
-            "slug": "globex",
-            "description": "We own the East Coast."
-        },
-        {
-            "name": "Soylent Corp",
-            "slug": "soylent",
-            "description": "Making food for the future."
-        },
-        {
-            "name": "Initech",
-            "slug": "initech",
-            "description": "Software for the soul."
-        },
-        {
-            "name": "Umbrella Corporation",
-            "slug": "umbrella",
-            "description": "Our business is life itself."
-        },
-        {
-            "name": "Massive Dynamic",
-            "slug": "massive-dynamic",
-            "description": "What don't we do?"
-        }
-    ],
-    "users": [
-        {
-            "email": "demo@example.com",
-            "password": "DemoPass1234!",
-            "first_name": "Demo",
-            "last_name": "User",
-            "is_superuser": false,
-            "organization_slug": "acme-corp",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "alice@acme.com",
-            "password": "Demo123!",
-            "first_name": "Alice",
-            "last_name": "Smith",
-            "is_superuser": false,
-            "organization_slug": "acme-corp",
-            "role": "admin",
-            "is_active": true
-        },
-        {
-            "email": "bob@acme.com",
-            "password": "Demo123!",
-            "first_name": "Bob",
-            "last_name": "Jones",
-            "is_superuser": false,
-            "organization_slug": "acme-corp",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "charlie@acme.com",
-            "password": "Demo123!",
-            "first_name": "Charlie",
-            "last_name": "Brown",
-            "is_superuser": false,
-            "organization_slug": "acme-corp",
-            "role": "member",
-            "is_active": false
-        },
-        {
-            "email": "diana@acme.com",
-            "password": "Demo123!",
-            "first_name": "Diana",
-            "last_name": "Prince",
-            "is_superuser": false,
-            "organization_slug": "acme-corp",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "carol@globex.com",
-            "password": "Demo123!",
-            "first_name": "Carol",
-            "last_name": "Williams",
-            "is_superuser": false,
-            "organization_slug": "globex",
-            "role": "owner",
-            "is_active": true
-        },
-        {
-            "email": "dan@globex.com",
-            "password": "Demo123!",
-            "first_name": "Dan",
-            "last_name": "Miller",
-            "is_superuser": false,
-            "organization_slug": "globex",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "ellen@globex.com",
-            "password": "Demo123!",
-            "first_name": "Ellen",
-            "last_name": "Ripley",
-            "is_superuser": false,
-            "organization_slug": "globex",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "fred@globex.com",
-            "password": "Demo123!",
-            "first_name": "Fred",
-            "last_name": "Flintstone",
-            "is_superuser": false,
-            "organization_slug": "globex",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "dave@soylent.com",
-            "password": "Demo123!",
-            "first_name": "Dave",
-            "last_name": "Brown",
-            "is_superuser": false,
-            "organization_slug": "soylent",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "gina@soylent.com",
-            "password": "Demo123!",
-            "first_name": "Gina",
-            "last_name": "Torres",
-            "is_superuser": false,
-            "organization_slug": "soylent",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "harry@soylent.com",
-            "password": "Demo123!",
-            "first_name": "Harry",
-            "last_name": "Potter",
-            "is_superuser": false,
-            "organization_slug": "soylent",
-            "role": "admin",
-            "is_active": true
-        },
-        {
-            "email": "eve@initech.com",
-            "password": "Demo123!",
-            "first_name": "Eve",
-            "last_name": "Davis",
-            "is_superuser": false,
-            "organization_slug": "initech",
-            "role": "admin",
-            "is_active": true
-        },
-        {
-            "email": "iris@initech.com",
-            "password": "Demo123!",
-            "first_name": "Iris",
-            "last_name": "West",
-            "is_superuser": false,
-            "organization_slug": "initech",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "jack@initech.com",
-            "password": "Demo123!",
-            "first_name": "Jack",
-            "last_name": "Sparrow",
-            "is_superuser": false,
-            "organization_slug": "initech",
-            "role": "member",
-            "is_active": false
-        },
-        {
-            "email": "frank@umbrella.com",
-            "password": "Demo123!",
-            "first_name": "Frank",
-            "last_name": "Miller",
-            "is_superuser": false,
-            "organization_slug": "umbrella",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "george@umbrella.com",
-            "password": "Demo123!",
-            "first_name": "George",
-            "last_name": "Costanza",
-            "is_superuser": false,
-            "organization_slug": "umbrella",
-            "role": "member",
-            "is_active": false
-        },
-        {
-            "email": "kate@umbrella.com",
-            "password": "Demo123!",
-            "first_name": "Kate",
-            "last_name": "Bishop",
-            "is_superuser": false,
-            "organization_slug": "umbrella",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "leo@massive.com",
-            "password": "Demo123!",
-            "first_name": "Leo",
-            "last_name": "Messi",
-            "is_superuser": false,
-            "organization_slug": "massive-dynamic",
-            "role": "owner",
-            "is_active": true
-        },
-        {
-            "email": "mary@massive.com",
-            "password": "Demo123!",
-            "first_name": "Mary",
-            "last_name": "Jane",
-            "is_superuser": false,
-            "organization_slug": "massive-dynamic",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "nathan@massive.com",
-            "password": "Demo123!",
-            "first_name": "Nathan",
-            "last_name": "Drake",
-            "is_superuser": false,
-            "organization_slug": "massive-dynamic",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "olivia@massive.com",
-            "password": "Demo123!",
-            "first_name": "Olivia",
-            "last_name": "Dunham",
-            "is_superuser": false,
-            "organization_slug": "massive-dynamic",
-            "role": "admin",
-            "is_active": true
-        },
-        {
-            "email": "peter@massive.com",
-            "password": "Demo123!",
-            "first_name": "Peter",
-            "last_name": "Parker",
-            "is_superuser": false,
-            "organization_slug": "massive-dynamic",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "quinn@massive.com",
-            "password": "Demo123!",
-            "first_name": "Quinn",
-            "last_name": "Mallory",
-            "is_superuser": false,
-            "organization_slug": "massive-dynamic",
-            "role": "member",
-            "is_active": true
-        },
-        {
-            "email": "grace@example.com",
-            "password": "Demo123!",
-            "first_name": "Grace",
-            "last_name": "Hopper",
-            "is_superuser": false,
-            "organization_slug": null,
-            "role": null,
-            "is_active": true
-        },
-        {
-            "email": "heidi@example.com",
-            "password": "Demo123!",
-            "first_name": "Heidi",
-            "last_name": "Klum",
-            "is_superuser": false,
-            "organization_slug": null,
-            "role": null,
-            "is_active": true
-        },
-        {
-            "email": "ivan@example.com",
-            "password": "Demo123!",
-            "first_name": "Ivan",
-            "last_name": "Drago",
-            "is_superuser": false,
-            "organization_slug": null,
-            "role": null,
-            "is_active": false
-        },
-        {
-            "email": "rachel@example.com",
-            "password": "Demo123!",
-            "first_name": "Rachel",
-            "last_name": "Green",
-            "is_superuser": false,
-            "organization_slug": null,
-            "role": null,
-            "is_active": true
-        },
-        {
-            "email": "sam@example.com",
-            "password": "Demo123!",
-            "first_name": "Sam",
-            "last_name": "Wilson",
-            "is_superuser": false,
-            "organization_slug": null,
-            "role": null,
-            "is_active": true
-        },
-        {
-            "email": "tony@example.com",
-            "password": "Demo123!",
-            "first_name": "Tony",
-            "last_name": "Stark",
-            "is_superuser": false,
-            "organization_slug": null,
-            "role": null,
-            "is_active": true
-        },
-        {
-            "email": "una@example.com",
-            "password": "Demo123!",
-            "first_name": "Una",
-            "last_name": "Chin-Riley",
-            "is_superuser": false,
-            "organization_slug": null,
-            "role": null,
-            "is_active": false
-        },
-        {
-            "email": "victor@example.com",
-            "password": "Demo123!",
-            "first_name": "Victor",
-            "last_name": "Von Doom",
-            "is_superuser": false,
-            "organization_slug": null,
-            "role": null,
-            "is_active": true
-        },
-        {
-            "email": "wanda@example.com",
-            "password": "Demo123!",
-            "first_name": "Wanda",
-            "last_name": "Maximoff",
-            "is_superuser": false,
-            "organization_slug": null,
-            "role": null,
-            "is_active": true
-        }
-    ]
-}

backend/app/crud/syndarix/agent_type.py

@@ -43,6 +43,13 @@ class CRUDAgentType(CRUDBase[AgentType, AgentTypeCreate, AgentTypeUpdate]):
                 mcp_servers=obj_in.mcp_servers,
                 tool_permissions=obj_in.tool_permissions,
                 is_active=obj_in.is_active,
+                # Category and display fields
+                category=obj_in.category.value if obj_in.category else None,
+                icon=obj_in.icon,
+                color=obj_in.color,
+                sort_order=obj_in.sort_order,
+                typical_tasks=obj_in.typical_tasks,
+                collaboration_hints=obj_in.collaboration_hints,
             )
             db.add(db_obj)
             await db.commit()
@@ -68,6 +75,7 @@ class CRUDAgentType(CRUDBase[AgentType, AgentTypeCreate, AgentTypeUpdate]):
         skip: int = 0,
         limit: int = 100,
         is_active: bool | None = None,
+        category: str | None = None,
         search: str | None = None,
         sort_by: str = "created_at",
         sort_order: str = "desc",
@@ -85,6 +93,9 @@ class CRUDAgentType(CRUDBase[AgentType, AgentTypeCreate, AgentTypeUpdate]):
             if is_active is not None:
                 query = query.where(AgentType.is_active == is_active)
 
+            if category:
+                query = query.where(AgentType.category == category)
+
             if search:
                 search_filter = or_(
                     AgentType.name.ilike(f"%{search}%"),
@@ -162,6 +173,7 @@ class CRUDAgentType(CRUDBase[AgentType, AgentTypeCreate, AgentTypeUpdate]):
         skip: int = 0,
         limit: int = 100,
         is_active: bool | None = None,
+        category: str | None = None,
         search: str | None = None,
     ) -> tuple[list[dict[str, Any]], int]:
         """
@@ -177,6 +189,7 @@ class CRUDAgentType(CRUDBase[AgentType, AgentTypeCreate, AgentTypeUpdate]):
                 skip=skip,
                 limit=limit,
                 is_active=is_active,
+                category=category,
                 search=search,
             )
 
@@ -260,6 +273,44 @@ class CRUDAgentType(CRUDBase[AgentType, AgentTypeCreate, AgentTypeUpdate]):
             )
             raise
 
+    async def get_grouped_by_category(
+        self,
+        db: AsyncSession,
+        *,
+        is_active: bool = True,
+    ) -> dict[str, list[AgentType]]:
+        """
+        Get agent types grouped by category, sorted by sort_order within each group.
+
+        Args:
+            db: Database session
+            is_active: Filter by active status (default: True)
+
+        Returns:
+            Dictionary mapping category to list of agent types
+        """
+        try:
+            query = (
+                select(AgentType)
+                .where(AgentType.is_active == is_active)
+                .order_by(AgentType.category, AgentType.sort_order, AgentType.name)
+            )
+            result = await db.execute(query)
+            agent_types = list(result.scalars().all())
+
+            # Group by category
+            grouped: dict[str, list[AgentType]] = {}
+            for at in agent_types:
+                cat: str = str(at.category) if at.category else "uncategorized"
+                if cat not in grouped:
+                    grouped[cat] = []
+                grouped[cat].append(at)
+
+            return grouped
+        except Exception as e:
+            logger.error(f"Error getting grouped agent types: {e!s}", exc_info=True)
+            raise
+
 
 # Create a singleton instance for use across the application
 agent_type = CRUDAgentType(AgentType)

backend/app/init_db.py

@@ -3,27 +3,48 @@
 Async database initialization script.
 
 Creates the first superuser if configured and doesn't already exist.
+Seeds default agent types (production data) and demo data (when DEMO_MODE is enabled).
 """
 
 import asyncio
 import json
 import logging
 import random
-from datetime import UTC, datetime, timedelta
+from datetime import UTC, date, datetime, timedelta
 from pathlib import Path
 
 from sqlalchemy import select, text
+from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.core.config import settings
 from app.core.database import SessionLocal, engine
+from app.crud.syndarix.agent_type import agent_type as agent_type_crud
 from app.crud.user import user as user_crud
 from app.models.organization import Organization
+from app.models.syndarix import AgentInstance, AgentType, Issue, Project, Sprint
+from app.models.syndarix.enums import (
+    AgentStatus,
+    AutonomyLevel,
+    ClientMode,
+    IssuePriority,
+    IssueStatus,
+    IssueType,
+    ProjectComplexity,
+    ProjectStatus,
+    SprintStatus,
+)
 from app.models.user import User
 from app.models.user_organization import UserOrganization
+from app.schemas.syndarix import AgentTypeCreate
 from app.schemas.users import UserCreate
 
 logger = logging.getLogger(__name__)
 
+# Data file paths
+DATA_DIR = Path(__file__).parent.parent / "data"
+DEFAULT_AGENT_TYPES_PATH = DATA_DIR / "default_agent_types.json"
+DEMO_DATA_PATH = DATA_DIR / "demo_data.json"
+
 
 async def init_db() -> User | None:
     """
@@ -54,28 +75,29 @@ async def init_db() -> User | None:
 
             if existing_user:
                 logger.info(f"Superuser already exists: {existing_user.email}")
-                return existing_user
+            else:
+                # Create superuser if doesn't exist
+                user_in = UserCreate(
+                    email=superuser_email,
+                    password=superuser_password,
+                    first_name="Admin",
+                    last_name="User",
+                    is_superuser=True,
+                )
 
-            # Create superuser if doesn't exist
-            user_in = UserCreate(
-                email=superuser_email,
-                password=superuser_password,
-                first_name="Admin",
-                last_name="User",
-                is_superuser=True,
-            )
+                existing_user = await user_crud.create(session, obj_in=user_in)
+                await session.commit()
+                await session.refresh(existing_user)
+                logger.info(f"Created first superuser: {existing_user.email}")
 
-            user = await user_crud.create(session, obj_in=user_in)
-            await session.commit()
-            await session.refresh(user)
+            # ALWAYS load default agent types (production data)
+            await load_default_agent_types(session)
 
-            logger.info(f"Created first superuser: {user.email}")
-
-            # Create demo data if in demo mode
+            # Only load demo data if in demo mode
             if settings.DEMO_MODE:
                 await load_demo_data(session)
 
-            return user
+            return existing_user
 
         except Exception as e:
             await session.rollback()
@@ -88,26 +110,96 @@ def _load_json_file(path: Path):
         return json.load(f)
 
 
-async def load_demo_data(session):
-    """Load demo data from JSON file."""
-    demo_data_path = Path(__file__).parent / "core" / "demo_data.json"
-    if not demo_data_path.exists():
-        logger.warning(f"Demo data file not found: {demo_data_path}")
+async def load_default_agent_types(session: AsyncSession) -> None:
+    """
+    Load default agent types from JSON file.
+
+    These are production defaults - created only if they don't exist, never overwritten.
+    This allows users to customize agent types without worrying about server restarts.
+    """
+    if not DEFAULT_AGENT_TYPES_PATH.exists():
+        logger.warning(
+            f"Default agent types file not found: {DEFAULT_AGENT_TYPES_PATH}"
+        )
         return
 
     try:
-        # Use asyncio.to_thread to avoid blocking the event loop
-        data = await asyncio.to_thread(_load_json_file, demo_data_path)
+        data = await asyncio.to_thread(_load_json_file, DEFAULT_AGENT_TYPES_PATH)
 
-        # Create Organizations
-        org_map = {}
-        for org_data in data.get("organizations", []):
-            # Check if org exists
-            result = await session.execute(
-                text("SELECT * FROM organizations WHERE slug = :slug"),
-                {"slug": org_data["slug"]},
+        for agent_type_data in data:
+            slug = agent_type_data["slug"]
+
+            # Check if agent type already exists
+            existing = await agent_type_crud.get_by_slug(session, slug=slug)
+
+            if existing:
+                logger.debug(f"Agent type already exists: {agent_type_data['name']}")
+                continue
+
+            # Create the agent type
+            agent_type_in = AgentTypeCreate(
+                name=agent_type_data["name"],
+                slug=slug,
+                description=agent_type_data.get("description"),
+                expertise=agent_type_data.get("expertise", []),
+                personality_prompt=agent_type_data["personality_prompt"],
+                primary_model=agent_type_data["primary_model"],
+                fallback_models=agent_type_data.get("fallback_models", []),
+                model_params=agent_type_data.get("model_params", {}),
+                mcp_servers=agent_type_data.get("mcp_servers", []),
+                tool_permissions=agent_type_data.get("tool_permissions", {}),
+                is_active=agent_type_data.get("is_active", True),
+                # Category and display fields
+                category=agent_type_data.get("category"),
+                icon=agent_type_data.get("icon", "bot"),
+                color=agent_type_data.get("color", "#3B82F6"),
+                sort_order=agent_type_data.get("sort_order", 0),
+                typical_tasks=agent_type_data.get("typical_tasks", []),
+                collaboration_hints=agent_type_data.get("collaboration_hints", []),
             )
-            existing_org = result.first()
+
+            await agent_type_crud.create(session, obj_in=agent_type_in)
+            logger.info(f"Created default agent type: {agent_type_data['name']}")
+
+        logger.info("Default agent types loaded successfully")
+
+    except Exception as e:
+        logger.error(f"Error loading default agent types: {e}")
+        raise
+
+
+async def load_demo_data(session: AsyncSession) -> None:
+    """
+    Load demo data from JSON file.
+
+    Only runs when DEMO_MODE is enabled. Creates demo organizations, users,
+    projects, sprints, agent instances, and issues.
+    """
+    if not DEMO_DATA_PATH.exists():
+        logger.warning(f"Demo data file not found: {DEMO_DATA_PATH}")
+        return
+
+    try:
+        data = await asyncio.to_thread(_load_json_file, DEMO_DATA_PATH)
+
+        # Build lookup maps for FK resolution
+        org_map: dict[str, Organization] = {}
+        user_map: dict[str, User] = {}
+        project_map: dict[str, Project] = {}
+        sprint_map: dict[str, Sprint] = {}  # key: "project_slug:sprint_number"
+        agent_type_map: dict[str, AgentType] = {}
+        agent_instance_map: dict[
+            str, AgentInstance
+        ] = {}  # key: "project_slug:agent_name"
+
+        # ========================
+        # 1. Create Organizations
+        # ========================
+        for org_data in data.get("organizations", []):
+            org_result = await session.execute(
+                select(Organization).where(Organization.slug == org_data["slug"])
+            )
+            existing_org = org_result.scalar_one_or_none()
 
             if not existing_org:
                 org = Organization(
@@ -117,29 +209,20 @@ async def load_demo_data(session):
                     is_active=True,
                 )
                 session.add(org)
-                await session.flush()  # Flush to get ID
-                org_map[org.slug] = org
+                await session.flush()
+                org_map[str(org.slug)] = org
                 logger.info(f"Created demo organization: {org.name}")
             else:
-                # We can't easily get the ORM object from raw SQL result for map without querying again or mapping
-                # So let's just query it properly if we need it for relationships
-                # But for simplicity in this script, let's just assume we created it or it exists.
-                # To properly map for users, we need the ID.
-                # Let's use a simpler approach: just try to create, if slug conflict, skip.
-                pass
+                org_map[str(existing_org.slug)] = existing_org
 
-        # Re-query all orgs to build map for users
-        result = await session.execute(select(Organization))
-        orgs = result.scalars().all()
-        org_map = {org.slug: org for org in orgs}
-
-        # Create Users
+        # ========================
+        # 2. Create Users
+        # ========================
         for user_data in data.get("users", []):
             existing_user = await user_crud.get_by_email(
                 session, email=user_data["email"]
             )
             if not existing_user:
-                # Create user
                 user_in = UserCreate(
                     email=user_data["email"],
                     password=user_data["password"],
@@ -151,17 +234,13 @@ async def load_demo_data(session):
                 user = await user_crud.create(session, obj_in=user_in)
 
                 # Randomize created_at for demo data (last 30 days)
-                # This makes the charts look more realistic
                 days_ago = random.randint(0, 30)  # noqa: S311
                 random_time = datetime.now(UTC) - timedelta(days=days_ago)
-                # Add some random hours/minutes variation
                 random_time = random_time.replace(
                     hour=random.randint(0, 23),  # noqa: S311
                     minute=random.randint(0, 59),  # noqa: S311
                 )
 
-                # Update the timestamp and is_active directly in the database
-                # We do this to ensure the values are persisted correctly
                 await session.execute(
                     text(
                         "UPDATE users SET created_at = :created_at, is_active = :is_active WHERE id = :user_id"
@@ -174,7 +253,7 @@ async def load_demo_data(session):
                 )
 
                 logger.info(
-                    f"Created demo user: {user.email} (created {days_ago} days ago, active={user_data.get('is_active', True)})"
+                    f"Created demo user: {user.email} (created {days_ago} days ago)"
                 )
 
                 # Add to organization if specified
@@ -182,19 +261,228 @@ async def load_demo_data(session):
                 role = user_data.get("role")
                 if org_slug and org_slug in org_map and role:
                     org = org_map[org_slug]
-                    # Check if membership exists (it shouldn't for new user)
                     member = UserOrganization(
                         user_id=user.id, organization_id=org.id, role=role
                     )
                     session.add(member)
                     logger.info(f"Added {user.email} to {org.name} as {role}")
+
+                user_map[str(user.email)] = user
             else:
-                logger.info(f"Demo user already exists: {existing_user.email}")
+                user_map[str(existing_user.email)] = existing_user
+                logger.debug(f"Demo user already exists: {existing_user.email}")
+
+        await session.flush()
+
+        # Add admin user to map with special "__admin__" key
+        # This allows demo data to reference the admin user as owner
+        superuser_email = settings.FIRST_SUPERUSER_EMAIL or "admin@example.com"
+        admin_user = await user_crud.get_by_email(session, email=superuser_email)
+        if admin_user:
+            user_map["__admin__"] = admin_user
+            user_map[str(admin_user.email)] = admin_user
+            logger.debug(f"Added admin user to map: {admin_user.email}")
+
+        # ========================
+        # 3. Load Agent Types Map (for FK resolution)
+        # ========================
+        agent_types_result = await session.execute(select(AgentType))
+        for at in agent_types_result.scalars().all():
+            agent_type_map[str(at.slug)] = at
+
+        # ========================
+        # 4. Create Projects
+        # ========================
+        for project_data in data.get("projects", []):
+            project_result = await session.execute(
+                select(Project).where(Project.slug == project_data["slug"])
+            )
+            existing_project = project_result.scalar_one_or_none()
+
+            if not existing_project:
+                # Resolve owner email to user ID
+                owner_id = None
+                owner_email = project_data.get("owner_email")
+                if owner_email and owner_email in user_map:
+                    owner_id = user_map[owner_email].id
+
+                project = Project(
+                    name=project_data["name"],
+                    slug=project_data["slug"],
+                    description=project_data.get("description"),
+                    owner_id=owner_id,
+                    autonomy_level=AutonomyLevel(
+                        project_data.get("autonomy_level", "milestone")
+                    ),
+                    status=ProjectStatus(project_data.get("status", "active")),
+                    complexity=ProjectComplexity(
+                        project_data.get("complexity", "medium")
+                    ),
+                    client_mode=ClientMode(project_data.get("client_mode", "auto")),
+                    settings=project_data.get("settings", {}),
+                )
+                session.add(project)
+                await session.flush()
+                project_map[str(project.slug)] = project
+                logger.info(f"Created demo project: {project.name}")
+            else:
+                project_map[str(existing_project.slug)] = existing_project
+                logger.debug(f"Demo project already exists: {existing_project.name}")
+
+        # ========================
+        # 5. Create Sprints
+        # ========================
+        for sprint_data in data.get("sprints", []):
+            project_slug = sprint_data["project_slug"]
+            sprint_number = sprint_data["number"]
+            sprint_key = f"{project_slug}:{sprint_number}"
+
+            if project_slug not in project_map:
+                logger.warning(f"Project not found for sprint: {project_slug}")
+                continue
+
+            sprint_project = project_map[project_slug]
+
+            # Check if sprint exists
+            sprint_result = await session.execute(
+                select(Sprint).where(
+                    Sprint.project_id == sprint_project.id,
+                    Sprint.number == sprint_number,
+                )
+            )
+            existing_sprint = sprint_result.scalar_one_or_none()
+
+            if not existing_sprint:
+                sprint = Sprint(
+                    project_id=sprint_project.id,
+                    name=sprint_data["name"],
+                    number=sprint_number,
+                    goal=sprint_data.get("goal"),
+                    start_date=date.fromisoformat(sprint_data["start_date"]),
+                    end_date=date.fromisoformat(sprint_data["end_date"]),
+                    status=SprintStatus(sprint_data.get("status", "planned")),
+                    planned_points=sprint_data.get("planned_points"),
+                )
+                session.add(sprint)
+                await session.flush()
+                sprint_map[sprint_key] = sprint
+                logger.info(
+                    f"Created demo sprint: {sprint.name} for {sprint_project.name}"
+                )
+            else:
+                sprint_map[sprint_key] = existing_sprint
+                logger.debug(f"Demo sprint already exists: {existing_sprint.name}")
+
+        # ========================
+        # 6. Create Agent Instances
+        # ========================
+        for agent_data in data.get("agent_instances", []):
+            project_slug = agent_data["project_slug"]
+            agent_type_slug = agent_data["agent_type_slug"]
+            agent_name = agent_data["name"]
+            agent_key = f"{project_slug}:{agent_name}"
+
+            if project_slug not in project_map:
+                logger.warning(f"Project not found for agent: {project_slug}")
+                continue
+
+            if agent_type_slug not in agent_type_map:
+                logger.warning(f"Agent type not found: {agent_type_slug}")
+                continue
+
+            agent_project = project_map[project_slug]
+            agent_type = agent_type_map[agent_type_slug]
+
+            # Check if agent instance exists (by name within project)
+            agent_result = await session.execute(
+                select(AgentInstance).where(
+                    AgentInstance.project_id == agent_project.id,
+                    AgentInstance.name == agent_name,
+                )
+            )
+            existing_agent = agent_result.scalar_one_or_none()
+
+            if not existing_agent:
+                agent_instance = AgentInstance(
+                    project_id=agent_project.id,
+                    agent_type_id=agent_type.id,
+                    name=agent_name,
+                    status=AgentStatus(agent_data.get("status", "idle")),
+                    current_task=agent_data.get("current_task"),
+                )
+                session.add(agent_instance)
+                await session.flush()
+                agent_instance_map[agent_key] = agent_instance
+                logger.info(
+                    f"Created demo agent: {agent_name} ({agent_type.name}) "
+                    f"for {agent_project.name}"
+                )
+            else:
+                agent_instance_map[agent_key] = existing_agent
+                logger.debug(f"Demo agent already exists: {existing_agent.name}")
+
+        # ========================
+        # 7. Create Issues
+        # ========================
+        for issue_data in data.get("issues", []):
+            project_slug = issue_data["project_slug"]
+
+            if project_slug not in project_map:
+                logger.warning(f"Project not found for issue: {project_slug}")
+                continue
+
+            issue_project = project_map[project_slug]
+
+            # Check if issue exists (by title within project - simple heuristic)
+            issue_result = await session.execute(
+                select(Issue).where(
+                    Issue.project_id == issue_project.id,
+                    Issue.title == issue_data["title"],
+                )
+            )
+            existing_issue = issue_result.scalar_one_or_none()
+
+            if not existing_issue:
+                # Resolve sprint
+                sprint_id = None
+                sprint_number = issue_data.get("sprint_number")
+                if sprint_number:
+                    sprint_key = f"{project_slug}:{sprint_number}"
+                    if sprint_key in sprint_map:
+                        sprint_id = sprint_map[sprint_key].id
+
+                # Resolve assigned agent
+                assigned_agent_id = None
+                assigned_agent_name = issue_data.get("assigned_agent_name")
+                if assigned_agent_name:
+                    agent_key = f"{project_slug}:{assigned_agent_name}"
+                    if agent_key in agent_instance_map:
+                        assigned_agent_id = agent_instance_map[agent_key].id
+
+                issue = Issue(
+                    project_id=issue_project.id,
+                    sprint_id=sprint_id,
+                    type=IssueType(issue_data.get("type", "task")),
+                    title=issue_data["title"],
+                    body=issue_data.get("body", ""),
+                    status=IssueStatus(issue_data.get("status", "open")),
+                    priority=IssuePriority(issue_data.get("priority", "medium")),
+                    labels=issue_data.get("labels", []),
+                    story_points=issue_data.get("story_points"),
+                    assigned_agent_id=assigned_agent_id,
+                )
+                session.add(issue)
+                logger.info(f"Created demo issue: {issue.title[:50]}...")
+            else:
+                logger.debug(
+                    f"Demo issue already exists: {existing_issue.title[:50]}..."
+                )
 
         await session.commit()
         logger.info("Demo data loaded successfully")
 
     except Exception as e:
+        await session.rollback()
         logger.error(f"Error loading demo data: {e}")
         raise
 
@@ -210,12 +498,12 @@ async def main():
     try:
         user = await init_db()
         if user:
-            print("✓ Database initialized successfully")
-            print(f"✓ Superuser: {user.email}")
+            print("Database initialized successfully")
+            print(f"Superuser: {user.email}")
         else:
-            print("✗ Failed to initialize database")
+            print("Failed to initialize database")
     except Exception as e:
-        print(f"✗ Error initializing database: {e}")
+        print(f"Error initializing database: {e}")
         raise
     finally:
         # Close the engine

backend/app/models/__init__.py

@@ -8,6 +8,19 @@ from app.core.database import Base
 
 from .base import TimestampMixin, UUIDMixin
 
+# Memory system models
+from .memory import (
+    ConsolidationStatus,
+    ConsolidationType,
+    Episode,
+    EpisodeOutcome,
+    Fact,
+    MemoryConsolidationLog,
+    Procedure,
+    ScopeType,
+    WorkingMemory,
+)
+
 # OAuth models (client mode - authenticate via Google/GitHub)
 from .oauth_account import OAuthAccount
 
@@ -37,7 +50,14 @@ __all__ = [
     "AgentInstance",
     "AgentType",
     "Base",
+    # Memory models
+    "ConsolidationStatus",
+    "ConsolidationType",
+    "Episode",
+    "EpisodeOutcome",
+    "Fact",
     "Issue",
+    "MemoryConsolidationLog",
     "OAuthAccount",
     "OAuthAuthorizationCode",
     "OAuthClient",
@@ -46,11 +66,14 @@ __all__ = [
     "OAuthState",
     "Organization",
     "OrganizationRole",
+    "Procedure",
     "Project",
+    "ScopeType",
     "Sprint",
     "TimestampMixin",
     "UUIDMixin",
     "User",
     "UserOrganization",
     "UserSession",
+    "WorkingMemory",
 ]

backend/app/models/memory/__init__.py

@@ -0,0 +1,32 @@
+# app/models/memory/__init__.py
+"""
+Memory System Database Models.
+
+Provides SQLAlchemy models for the Agent Memory System:
+- WorkingMemory: Key-value storage with TTL
+- Episode: Experiential memories
+- Fact: Semantic knowledge triples
+- Procedure: Learned skills
+- MemoryConsolidationLog: Consolidation job tracking
+"""
+
+from .consolidation import MemoryConsolidationLog
+from .enums import ConsolidationStatus, ConsolidationType, EpisodeOutcome, ScopeType
+from .episode import Episode
+from .fact import Fact
+from .procedure import Procedure
+from .working_memory import WorkingMemory
+
+__all__ = [
+    # Enums
+    "ConsolidationStatus",
+    "ConsolidationType",
+    # Models
+    "Episode",
+    "EpisodeOutcome",
+    "Fact",
+    "MemoryConsolidationLog",
+    "Procedure",
+    "ScopeType",
+    "WorkingMemory",
+]

backend/app/models/memory/consolidation.py

@@ -0,0 +1,72 @@
+# app/models/memory/consolidation.py
+"""
+Memory Consolidation Log database model.
+
+Tracks memory consolidation jobs that transfer knowledge
+between memory tiers.
+"""
+
+from sqlalchemy import Column, DateTime, Enum, Index, Integer, Text
+
+from app.models.base import Base, TimestampMixin, UUIDMixin
+
+from .enums import ConsolidationStatus, ConsolidationType
+
+
+class MemoryConsolidationLog(Base, UUIDMixin, TimestampMixin):
+    """
+    Memory consolidation job log.
+
+    Tracks consolidation operations:
+    - Working -> Episodic (session end)
+    - Episodic -> Semantic (fact extraction)
+    - Episodic -> Procedural (procedure learning)
+    - Pruning (removing low-value memories)
+    """
+
+    __tablename__ = "memory_consolidation_log"
+
+    # Consolidation type
+    consolidation_type: Column[ConsolidationType] = Column(
+        Enum(ConsolidationType),
+        nullable=False,
+        index=True,
+    )
+
+    # Counts
+    source_count = Column(Integer, nullable=False, default=0)
+    result_count = Column(Integer, nullable=False, default=0)
+
+    # Timing
+    started_at = Column(DateTime(timezone=True), nullable=False)
+    completed_at = Column(DateTime(timezone=True), nullable=True)
+
+    # Status
+    status: Column[ConsolidationStatus] = Column(
+        Enum(ConsolidationStatus),
+        nullable=False,
+        default=ConsolidationStatus.PENDING,
+        index=True,
+    )
+
+    # Error details if failed
+    error = Column(Text, nullable=True)
+
+    __table_args__ = (
+        # Query patterns
+        Index("ix_consolidation_type_status", "consolidation_type", "status"),
+        Index("ix_consolidation_started", "started_at"),
+    )
+
+    @property
+    def duration_seconds(self) -> float | None:
+        """Calculate duration of the consolidation job."""
+        if self.completed_at is None or self.started_at is None:
+            return None
+        return (self.completed_at - self.started_at).total_seconds()
+
+    def __repr__(self) -> str:
+        return (
+            f"<MemoryConsolidationLog {self.id} "
+            f"type={self.consolidation_type.value} status={self.status.value}>"
+        )

backend/app/models/memory/enums.py

@@ -0,0 +1,73 @@
+# app/models/memory/enums.py
+"""
+Enums for Memory System database models.
+
+These enums define the database-level constraints for memory types
+and scoping levels.
+"""
+
+from enum import Enum as PyEnum
+
+
+class ScopeType(str, PyEnum):
+    """
+    Memory scope levels matching the memory service types.
+
+    GLOBAL: System-wide memories accessible by all
+    PROJECT: Project-scoped memories
+    AGENT_TYPE: Type-specific memories (shared by instances of same type)
+    AGENT_INSTANCE: Instance-specific memories
+    SESSION: Session-scoped ephemeral memories
+    """
+
+    GLOBAL = "global"
+    PROJECT = "project"
+    AGENT_TYPE = "agent_type"
+    AGENT_INSTANCE = "agent_instance"
+    SESSION = "session"
+
+
+class EpisodeOutcome(str, PyEnum):
+    """
+    Outcome of an episode (task execution).
+
+    SUCCESS: Task completed successfully
+    FAILURE: Task failed
+    PARTIAL: Task partially completed
+    """
+
+    SUCCESS = "success"
+    FAILURE = "failure"
+    PARTIAL = "partial"
+
+
+class ConsolidationType(str, PyEnum):
+    """
+    Types of memory consolidation operations.
+
+    WORKING_TO_EPISODIC: Transfer session state to episodic
+    EPISODIC_TO_SEMANTIC: Extract facts from episodes
+    EPISODIC_TO_PROCEDURAL: Extract procedures from episodes
+    PRUNING: Remove low-value memories
+    """
+
+    WORKING_TO_EPISODIC = "working_to_episodic"
+    EPISODIC_TO_SEMANTIC = "episodic_to_semantic"
+    EPISODIC_TO_PROCEDURAL = "episodic_to_procedural"
+    PRUNING = "pruning"
+
+
+class ConsolidationStatus(str, PyEnum):
+    """
+    Status of a consolidation job.
+
+    PENDING: Job is queued
+    RUNNING: Job is currently executing
+    COMPLETED: Job finished successfully
+    FAILED: Job failed with errors
+    """
+
+    PENDING = "pending"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    FAILED = "failed"

backend/app/models/memory/episode.py

@@ -0,0 +1,139 @@
+# app/models/memory/episode.py
+"""
+Episode database model.
+
+Stores experiential memories - records of past task executions
+with context, actions, outcomes, and lessons learned.
+"""
+
+from sqlalchemy import (
+    BigInteger,
+    CheckConstraint,
+    Column,
+    DateTime,
+    Enum,
+    Float,
+    ForeignKey,
+    Index,
+    String,
+    Text,
+)
+from sqlalchemy.dialects.postgresql import (
+    JSONB,
+    UUID as PGUUID,
+)
+from sqlalchemy.orm import relationship
+
+from app.models.base import Base, TimestampMixin, UUIDMixin
+
+from .enums import EpisodeOutcome
+
+# Import pgvector type - will be available after migration enables extension
+try:
+    from pgvector.sqlalchemy import Vector  # type: ignore[import-not-found]
+except ImportError:
+    # Fallback for environments without pgvector
+    Vector = None
+
+
+class Episode(Base, UUIDMixin, TimestampMixin):
+    """
+    Episodic memory model.
+
+    Records experiential memories from agent task execution:
+    - What task was performed
+    - What actions were taken
+    - What was the outcome
+    - What lessons were learned
+    """
+
+    __tablename__ = "episodes"
+
+    # Foreign keys
+    project_id = Column(
+        PGUUID(as_uuid=True),
+        ForeignKey("projects.id", ondelete="CASCADE"),
+        nullable=False,
+        index=True,
+    )
+
+    agent_instance_id = Column(
+        PGUUID(as_uuid=True),
+        ForeignKey("agent_instances.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+
+    agent_type_id = Column(
+        PGUUID(as_uuid=True),
+        ForeignKey("agent_types.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+
+    # Session reference
+    session_id = Column(String(255), nullable=False, index=True)
+
+    # Task information
+    task_type = Column(String(100), nullable=False, index=True)
+    task_description = Column(Text, nullable=False)
+
+    # Actions taken (list of action dictionaries)
+    actions = Column(JSONB, default=list, nullable=False)
+
+    # Context summary
+    context_summary = Column(Text, nullable=False)
+
+    # Outcome
+    outcome: Column[EpisodeOutcome] = Column(
+        Enum(EpisodeOutcome),
+        nullable=False,
+        index=True,
+    )
+    outcome_details = Column(Text, nullable=True)
+
+    # Metrics
+    duration_seconds = Column(Float, nullable=False, default=0.0)
+    tokens_used = Column(BigInteger, nullable=False, default=0)
+
+    # Learning
+    lessons_learned = Column(JSONB, default=list, nullable=False)
+    importance_score = Column(Float, nullable=False, default=0.5, index=True)
+
+    # Vector embedding for semantic search
+    # Using 1536 dimensions for OpenAI text-embedding-3-small
+    embedding = Column(Vector(1536) if Vector else Text, nullable=True)
+
+    # When the episode occurred
+    occurred_at = Column(DateTime(timezone=True), nullable=False, index=True)
+
+    # Relationships
+    project = relationship("Project", foreign_keys=[project_id])
+    agent_instance = relationship("AgentInstance", foreign_keys=[agent_instance_id])
+    agent_type = relationship("AgentType", foreign_keys=[agent_type_id])
+
+    __table_args__ = (
+        # Primary query patterns
+        Index("ix_episodes_project_task", "project_id", "task_type"),
+        Index("ix_episodes_project_outcome", "project_id", "outcome"),
+        Index("ix_episodes_agent_task", "agent_instance_id", "task_type"),
+        Index("ix_episodes_project_time", "project_id", "occurred_at"),
+        # For importance-based pruning
+        Index("ix_episodes_importance_time", "importance_score", "occurred_at"),
+        # Data integrity constraints
+        CheckConstraint(
+            "importance_score >= 0.0 AND importance_score <= 1.0",
+            name="ck_episodes_importance_range",
+        ),
+        CheckConstraint(
+            "duration_seconds >= 0.0",
+            name="ck_episodes_duration_positive",
+        ),
+        CheckConstraint(
+            "tokens_used >= 0",
+            name="ck_episodes_tokens_positive",
+        ),
+    )
+
+    def __repr__(self) -> str:
+        return f"<Episode {self.id} task={self.task_type} outcome={self.outcome.value}>"

backend/app/models/memory/fact.py

@@ -0,0 +1,120 @@
+# app/models/memory/fact.py
+"""
+Fact database model.
+
+Stores semantic memories - learned facts in subject-predicate-object
+triple format with confidence scores and source tracking.
+"""
+
+from sqlalchemy import (
+    CheckConstraint,
+    Column,
+    DateTime,
+    Float,
+    ForeignKey,
+    Index,
+    Integer,
+    String,
+    Text,
+    text,
+)
+from sqlalchemy.dialects.postgresql import (
+    JSONB,
+    UUID as PGUUID,
+)
+from sqlalchemy.orm import relationship
+
+from app.models.base import Base, TimestampMixin, UUIDMixin
+
+# Import pgvector type
+try:
+    from pgvector.sqlalchemy import Vector  # type: ignore[import-not-found]
+except ImportError:
+    Vector = None
+
+
+class Fact(Base, UUIDMixin, TimestampMixin):
+    """
+    Semantic memory model.
+
+    Stores learned facts as subject-predicate-object triples:
+    - "FastAPI" - "uses" - "Starlette framework"
+    - "Project Alpha" - "requires" - "OAuth authentication"
+
+    Facts have confidence scores that decay over time and can be
+    reinforced when the same fact is learned again.
+    """
+
+    __tablename__ = "facts"
+
+    # Scoping: project_id is NULL for global facts
+    project_id = Column(
+        PGUUID(as_uuid=True),
+        ForeignKey("projects.id", ondelete="CASCADE"),
+        nullable=True,
+        index=True,
+    )
+
+    # Triple format
+    subject = Column(String(500), nullable=False, index=True)
+    predicate = Column(String(255), nullable=False, index=True)
+    object = Column(Text, nullable=False)
+
+    # Confidence score (0.0 to 1.0)
+    confidence = Column(Float, nullable=False, default=0.8, index=True)
+
+    # Source tracking: which episodes contributed to this fact (stored as JSONB array of UUID strings)
+    source_episode_ids: Column[list] = Column(JSONB, default=list, nullable=False)
+
+    # Learning history
+    first_learned = Column(DateTime(timezone=True), nullable=False)
+    last_reinforced = Column(DateTime(timezone=True), nullable=False)
+    reinforcement_count = Column(Integer, nullable=False, default=1)
+
+    # Vector embedding for semantic search
+    embedding = Column(Vector(1536) if Vector else Text, nullable=True)
+
+    # Relationships
+    project = relationship("Project", foreign_keys=[project_id])
+
+    __table_args__ = (
+        # Unique constraint on triple within project scope
+        Index(
+            "ix_facts_unique_triple",
+            "project_id",
+            "subject",
+            "predicate",
+            "object",
+            unique=True,
+            postgresql_where=text("project_id IS NOT NULL"),
+        ),
+        # Unique constraint on triple for global facts (project_id IS NULL)
+        Index(
+            "ix_facts_unique_triple_global",
+            "subject",
+            "predicate",
+            "object",
+            unique=True,
+            postgresql_where=text("project_id IS NULL"),
+        ),
+        # Query patterns
+        Index("ix_facts_subject_predicate", "subject", "predicate"),
+        Index("ix_facts_project_subject", "project_id", "subject"),
+        Index("ix_facts_confidence_time", "confidence", "last_reinforced"),
+        # Note: subject already has index=True on Column definition, no need for explicit index
+        # Data integrity constraints
+        CheckConstraint(
+            "confidence >= 0.0 AND confidence <= 1.0",
+            name="ck_facts_confidence_range",
+        ),
+        CheckConstraint(
+            "reinforcement_count >= 1",
+            name="ck_facts_reinforcement_positive",
+        ),
+    )
+
+    def __repr__(self) -> str:
+        return (
+            f"<Fact {self.id} '{self.subject}' - '{self.predicate}' - "
+            f"'{self.object[:50]}...' conf={self.confidence:.2f}>"
+        )

backend/app/models/memory/procedure.py

@@ -0,0 +1,129 @@
+# app/models/memory/procedure.py
+"""
+Procedure database model.
+
+Stores procedural memories - learned skills and procedures
+derived from successful task execution patterns.
+"""
+
+from sqlalchemy import (
+    CheckConstraint,
+    Column,
+    DateTime,
+    ForeignKey,
+    Index,
+    Integer,
+    String,
+    Text,
+)
+from sqlalchemy.dialects.postgresql import (
+    JSONB,
+    UUID as PGUUID,
+)
+from sqlalchemy.orm import relationship
+
+from app.models.base import Base, TimestampMixin, UUIDMixin
+
+# Import pgvector type
+try:
+    from pgvector.sqlalchemy import Vector  # type: ignore[import-not-found]
+except ImportError:
+    Vector = None
+
+
+class Procedure(Base, UUIDMixin, TimestampMixin):
+    """
+    Procedural memory model.
+
+    Stores learned procedures (skills) extracted from successful
+    task execution patterns:
+    - Name and trigger pattern for matching
+    - Step-by-step actions
+    - Success/failure tracking
+    """
+
+    __tablename__ = "procedures"
+
+    # Scoping
+    project_id = Column(
+        PGUUID(as_uuid=True),
+        ForeignKey("projects.id", ondelete="CASCADE"),
+        nullable=True,
+        index=True,
+    )
+
+    agent_type_id = Column(
+        PGUUID(as_uuid=True),
+        ForeignKey("agent_types.id", ondelete="SET NULL"),
+        nullable=True,
+        index=True,
+    )
+
+    # Procedure identification
+    name = Column(String(255), nullable=False, index=True)
+    trigger_pattern = Column(Text, nullable=False)
+
+    # Steps as JSON array of step objects
+    # Each step: {order, action, parameters, expected_outcome, fallback_action}
+    steps = Column(JSONB, default=list, nullable=False)
+
+    # Success tracking
+    success_count = Column(Integer, nullable=False, default=0)
+    failure_count = Column(Integer, nullable=False, default=0)
+
+    # Usage tracking
+    last_used = Column(DateTime(timezone=True), nullable=True, index=True)
+
+    # Vector embedding for semantic matching
+    embedding = Column(Vector(1536) if Vector else Text, nullable=True)
+
+    # Relationships
+    project = relationship("Project", foreign_keys=[project_id])
+    agent_type = relationship("AgentType", foreign_keys=[agent_type_id])
+
+    __table_args__ = (
+        # Unique procedure name within scope
+        Index(
+            "ix_procedures_unique_name",
+            "project_id",
+            "agent_type_id",
+            "name",
+            unique=True,
+        ),
+        # Query patterns
+        Index("ix_procedures_project_name", "project_id", "name"),
+        # Note: agent_type_id already has index=True on Column definition
+        # For finding best procedures
+        Index("ix_procedures_success_rate", "success_count", "failure_count"),
+        # Data integrity constraints
+        CheckConstraint(
+            "success_count >= 0",
+            name="ck_procedures_success_positive",
+        ),
+        CheckConstraint(
+            "failure_count >= 0",
+            name="ck_procedures_failure_positive",
+        ),
+    )
+
+    @property
+    def success_rate(self) -> float:
+        """Calculate the success rate of this procedure."""
+        # Snapshot values to avoid race conditions in concurrent access
+        success = self.success_count
+        failure = self.failure_count
+        total = success + failure
+        if total == 0:
+            return 0.0
+        return success / total
+
+    @property
+    def total_uses(self) -> int:
+        """Get total number of times this procedure was used."""
+        # Snapshot values for consistency
+        return self.success_count + self.failure_count
+
+    def __repr__(self) -> str:
+        return (
+            f"<Procedure {self.name} ({self.id}) success_rate={self.success_rate:.2%}>"
+        )

backend/app/models/memory/working_memory.py

@@ -0,0 +1,58 @@
+# app/models/memory/working_memory.py
+"""
+Working Memory database model.
+
+Stores ephemeral key-value data for active sessions with TTL support.
+Used as database backup when Redis is unavailable.
+"""
+
+from sqlalchemy import Column, DateTime, Enum, Index, String
+from sqlalchemy.dialects.postgresql import JSONB
+
+from app.models.base import Base, TimestampMixin, UUIDMixin
+
+from .enums import ScopeType
+
+
+class WorkingMemory(Base, UUIDMixin, TimestampMixin):
+    """
+    Working memory storage table.
+
+    Provides database-backed working memory as fallback when
+    Redis is unavailable. Supports TTL-based expiration.
+    """
+
+    __tablename__ = "working_memory"
+
+    # Scoping
+    scope_type: Column[ScopeType] = Column(
+        Enum(ScopeType),
+        nullable=False,
+        index=True,
+    )
+    scope_id = Column(String(255), nullable=False, index=True)
+
+    # Key-value storage
+    key = Column(String(255), nullable=False)
+    value = Column(JSONB, nullable=False)
+
+    # TTL support
+    expires_at = Column(DateTime(timezone=True), nullable=True, index=True)
+
+    __table_args__ = (
+        # Primary lookup: scope + key
+        Index(
+            "ix_working_memory_scope_key",
+            "scope_type",
+            "scope_id",
+            "key",
+            unique=True,
+        ),
+        # For cleanup of expired entries
+        Index("ix_working_memory_expires", "expires_at"),
+        # For listing all keys in a scope
+        Index("ix_working_memory_scope_list", "scope_type", "scope_id"),
+    )
+
+    def __repr__(self) -> str:
+        return f"<WorkingMemory {self.scope_type.value}:{self.scope_id}:{self.key}>"

backend/app/models/syndarix/agent_instance.py

@@ -62,7 +62,11 @@ class AgentInstance(Base, UUIDMixin, TimestampMixin):
 
     # Status tracking
     status: Column[AgentStatus] = Column(
-        Enum(AgentStatus),
+        Enum(
+            AgentStatus,
+            name="agent_status",
+            values_callable=lambda x: [e.value for e in x],
+        ),
         default=AgentStatus.IDLE,
         nullable=False,
         index=True,

backend/app/models/syndarix/agent_type.py

@@ -6,7 +6,7 @@ An AgentType is a template that defines the capabilities, personality,
 and model configuration for agent instances.
 """
 
-from sqlalchemy import Boolean, Column, Index, String, Text
+from sqlalchemy import Boolean, Column, Index, Integer, String, Text
 from sqlalchemy.dialects.postgresql import JSONB
 from sqlalchemy.orm import relationship
 
@@ -56,6 +56,24 @@ class AgentType(Base, UUIDMixin, TimestampMixin):
     # Whether this agent type is available for new instances
     is_active = Column(Boolean, default=True, nullable=False, index=True)
 
+    # Category for grouping agents (development, design, quality, etc.)
+    category = Column(String(50), nullable=True, index=True)
+
+    # Lucide icon identifier for UI display (e.g., "code", "palette", "shield")
+    icon = Column(String(50), nullable=True, default="bot")
+
+    # Hex color code for visual distinction (e.g., "#3B82F6")
+    color = Column(String(7), nullable=True, default="#3B82F6")
+
+    # Display ordering within category (lower = first)
+    sort_order = Column(Integer, nullable=False, default=0, index=True)
+
+    # List of typical tasks this agent excels at
+    typical_tasks = Column(JSONB, default=list, nullable=False)
+
+    # List of agent slugs that collaborate well with this type
+    collaboration_hints = Column(JSONB, default=list, nullable=False)
+
     # Relationships
     instances = relationship(
         "AgentInstance",
@@ -66,6 +84,7 @@ class AgentType(Base, UUIDMixin, TimestampMixin):
     __table_args__ = (
         Index("ix_agent_types_slug_active", "slug", "is_active"),
         Index("ix_agent_types_name_active", "name", "is_active"),
+        Index("ix_agent_types_category_sort", "category", "sort_order"),
     )
 
     def __repr__(self) -> str:

backend/app/models/syndarix/enums.py

@@ -167,3 +167,29 @@ class SprintStatus(str, PyEnum):
     IN_REVIEW = "in_review"
     COMPLETED = "completed"
     CANCELLED = "cancelled"
+
+
+class AgentTypeCategory(str, PyEnum):
+    """
+    Category classification for agent types.
+
+    Used for grouping and filtering agents in the UI.
+
+    DEVELOPMENT: Product, project, and engineering roles
+    DESIGN: UI/UX and design research roles
+    QUALITY: QA and security engineering
+    OPERATIONS: DevOps and MLOps
+    AI_ML: Machine learning and AI specialists
+    DATA: Data science and engineering
+    LEADERSHIP: Technical leadership roles
+    DOMAIN_EXPERT: Industry and domain specialists
+    """
+
+    DEVELOPMENT = "development"
+    DESIGN = "design"
+    QUALITY = "quality"
+    OPERATIONS = "operations"
+    AI_ML = "ai_ml"
+    DATA = "data"
+    LEADERSHIP = "leadership"
+    DOMAIN_EXPERT = "domain_expert"

backend/app/models/syndarix/issue.py

@@ -59,7 +59,9 @@ class Issue(Base, UUIDMixin, TimestampMixin):
 
     # Issue type (Epic, Story, Task, Bug)
     type: Column[IssueType] = Column(
-        Enum(IssueType),
+        Enum(
+            IssueType, name="issue_type", values_callable=lambda x: [e.value for e in x]
+        ),
         default=IssueType.TASK,
         nullable=False,
         index=True,
@@ -78,14 +80,22 @@ class Issue(Base, UUIDMixin, TimestampMixin):
 
     # Status and priority
     status: Column[IssueStatus] = Column(
-        Enum(IssueStatus),
+        Enum(
+            IssueStatus,
+            name="issue_status",
+            values_callable=lambda x: [e.value for e in x],
+        ),
         default=IssueStatus.OPEN,
         nullable=False,
         index=True,
     )
 
     priority: Column[IssuePriority] = Column(
-        Enum(IssuePriority),
+        Enum(
+            IssuePriority,
+            name="issue_priority",
+            values_callable=lambda x: [e.value for e in x],
+        ),
         default=IssuePriority.MEDIUM,
         nullable=False,
         index=True,
@@ -132,7 +142,11 @@ class Issue(Base, UUIDMixin, TimestampMixin):
 
     # Sync status with external tracker
     sync_status: Column[SyncStatus] = Column(
-        Enum(SyncStatus),
+        Enum(
+            SyncStatus,
+            name="sync_status",
+            values_callable=lambda x: [e.value for e in x],
+        ),
         default=SyncStatus.SYNCED,
         nullable=False,
         # Note: Index defined in __table_args__ as ix_issues_sync_status

backend/app/models/syndarix/project.py

@@ -35,28 +35,44 @@ class Project(Base, UUIDMixin, TimestampMixin):
     description = Column(Text, nullable=True)
 
     autonomy_level: Column[AutonomyLevel] = Column(
-        Enum(AutonomyLevel),
+        Enum(
+            AutonomyLevel,
+            name="autonomy_level",
+            values_callable=lambda x: [e.value for e in x],
+        ),
         default=AutonomyLevel.MILESTONE,
         nullable=False,
         index=True,
     )
 
     status: Column[ProjectStatus] = Column(
-        Enum(ProjectStatus),
+        Enum(
+            ProjectStatus,
+            name="project_status",
+            values_callable=lambda x: [e.value for e in x],
+        ),
         default=ProjectStatus.ACTIVE,
         nullable=False,
         index=True,
     )
 
     complexity: Column[ProjectComplexity] = Column(
-        Enum(ProjectComplexity),
+        Enum(
+            ProjectComplexity,
+            name="project_complexity",
+            values_callable=lambda x: [e.value for e in x],
+        ),
         default=ProjectComplexity.MEDIUM,
         nullable=False,
         index=True,
     )
 
     client_mode: Column[ClientMode] = Column(
-        Enum(ClientMode),
+        Enum(
+            ClientMode,
+            name="client_mode",
+            values_callable=lambda x: [e.value for e in x],
+        ),
         default=ClientMode.AUTO,
         nullable=False,
         index=True,

backend/app/models/syndarix/sprint.py

@@ -57,7 +57,11 @@ class Sprint(Base, UUIDMixin, TimestampMixin):
 
     # Status
     status: Column[SprintStatus] = Column(
-        Enum(SprintStatus),
+        Enum(
+            SprintStatus,
+            name="sprint_status",
+            values_callable=lambda x: [e.value for e in x],
+        ),
         default=SprintStatus.PLANNED,
         nullable=False,
         index=True,

backend/app/schemas/syndarix/agent_type.py

@@ -10,6 +10,8 @@ from uuid import UUID
 
 from pydantic import BaseModel, ConfigDict, Field, field_validator
 
+from app.models.syndarix.enums import AgentTypeCategory
+
 
 class AgentTypeBase(BaseModel):
     """Base agent type schema with common fields."""
@@ -26,6 +28,14 @@ class AgentTypeBase(BaseModel):
     tool_permissions: dict[str, Any] = Field(default_factory=dict)
     is_active: bool = True
 
+    # Category and display fields
+    category: AgentTypeCategory | None = None
+    icon: str | None = Field(None, max_length=50)
+    color: str | None = Field(None, pattern=r"^#[0-9A-Fa-f]{6}$")
+    sort_order: int = Field(default=0, ge=0, le=1000)
+    typical_tasks: list[str] = Field(default_factory=list)
+    collaboration_hints: list[str] = Field(default_factory=list)
+
     @field_validator("slug")
     @classmethod
     def validate_slug(cls, v: str | None) -> str | None:
@@ -62,6 +72,18 @@ class AgentTypeBase(BaseModel):
         """Validate MCP server list."""
         return [s.strip() for s in v if s.strip()]
 
+    @field_validator("typical_tasks")
+    @classmethod
+    def validate_typical_tasks(cls, v: list[str]) -> list[str]:
+        """Validate and normalize typical tasks list."""
+        return [t.strip() for t in v if t.strip()]
+
+    @field_validator("collaboration_hints")
+    @classmethod
+    def validate_collaboration_hints(cls, v: list[str]) -> list[str]:
+        """Validate and normalize collaboration hints (agent slugs)."""
+        return [h.strip().lower() for h in v if h.strip()]
+
 
 class AgentTypeCreate(AgentTypeBase):
     """Schema for creating a new agent type."""
@@ -87,6 +109,14 @@ class AgentTypeUpdate(BaseModel):
     tool_permissions: dict[str, Any] | None = None
     is_active: bool | None = None
 
+    # Category and display fields (all optional for updates)
+    category: AgentTypeCategory | None = None
+    icon: str | None = Field(None, max_length=50)
+    color: str | None = Field(None, pattern=r"^#[0-9A-Fa-f]{6}$")
+    sort_order: int | None = Field(None, ge=0, le=1000)
+    typical_tasks: list[str] | None = None
+    collaboration_hints: list[str] | None = None
+
     @field_validator("slug")
     @classmethod
     def validate_slug(cls, v: str | None) -> str | None:
@@ -119,6 +149,22 @@ class AgentTypeUpdate(BaseModel):
             return v
         return [e.strip().lower() for e in v if e.strip()]
 
+    @field_validator("typical_tasks")
+    @classmethod
+    def validate_typical_tasks(cls, v: list[str] | None) -> list[str] | None:
+        """Validate and normalize typical tasks list."""
+        if v is None:
+            return v
+        return [t.strip() for t in v if t.strip()]
+
+    @field_validator("collaboration_hints")
+    @classmethod
+    def validate_collaboration_hints(cls, v: list[str] | None) -> list[str] | None:
+        """Validate and normalize collaboration hints (agent slugs)."""
+        if v is None:
+            return v
+        return [h.strip().lower() for h in v if h.strip()]
+
 
 class AgentTypeInDB(AgentTypeBase):
     """Schema for agent type in database."""

backend/app/services/context/__init__.py

@@ -0,0 +1,182 @@
+"""
+Context Management Engine
+
+Sophisticated context assembly and optimization for LLM requests.
+Provides intelligent context selection, token budget management,
+and model-specific formatting.
+
+Usage:
+    from app.services.context import (
+        ContextSettings,
+        get_context_settings,
+        SystemContext,
+        KnowledgeContext,
+        ConversationContext,
+        TaskContext,
+        ToolContext,
+        TokenBudget,
+        BudgetAllocator,
+        TokenCalculator,
+    )
+
+    # Get settings
+    settings = get_context_settings()
+
+    # Create budget for a model
+    allocator = BudgetAllocator(settings)
+    budget = allocator.create_budget_for_model("claude-3-sonnet")
+
+    # Create context instances
+    system_ctx = SystemContext.create_persona(
+        name="Code Assistant",
+        description="You are a helpful code assistant.",
+        capabilities=["Write code", "Debug issues"],
+    )
+"""
+
+# Budget Management
+# Adapters
+from .adapters import (
+    ClaudeAdapter,
+    DefaultAdapter,
+    ModelAdapter,
+    OpenAIAdapter,
+    get_adapter,
+)
+
+# Assembly
+from .assembly import (
+    ContextPipeline,
+    PipelineMetrics,
+)
+from .budget import (
+    BudgetAllocator,
+    TokenBudget,
+    TokenCalculator,
+)
+
+# Cache
+from .cache import ContextCache
+
+# Compression
+from .compression import (
+    ContextCompressor,
+    TruncationResult,
+    TruncationStrategy,
+)
+
+# Configuration
+from .config import (
+    ContextSettings,
+    get_context_settings,
+    get_default_settings,
+    reset_context_settings,
+)
+
+# Engine
+from .engine import ContextEngine, create_context_engine
+
+# Exceptions
+from .exceptions import (
+    AssemblyTimeoutError,
+    BudgetExceededError,
+    CacheError,
+    CompressionError,
+    ContextError,
+    ContextNotFoundError,
+    FormattingError,
+    InvalidContextError,
+    ScoringError,
+    TokenCountError,
+)
+
+# Prioritization
+from .prioritization import (
+    ContextRanker,
+    RankingResult,
+)
+
+# Scoring
+from .scoring import (
+    BaseScorer,
+    CompositeScorer,
+    PriorityScorer,
+    RecencyScorer,
+    RelevanceScorer,
+    ScoredContext,
+)
+
+# Types
+from .types import (
+    AssembledContext,
+    BaseContext,
+    ContextPriority,
+    ContextType,
+    ConversationContext,
+    KnowledgeContext,
+    MemoryContext,
+    MemorySubtype,
+    MessageRole,
+    SystemContext,
+    TaskComplexity,
+    TaskContext,
+    TaskStatus,
+    ToolContext,
+    ToolResultStatus,
+)
+
+__all__ = [
+    "AssembledContext",
+    "AssemblyTimeoutError",
+    "BaseContext",
+    "BaseScorer",
+    "BudgetAllocator",
+    "BudgetExceededError",
+    "CacheError",
+    "ClaudeAdapter",
+    "CompositeScorer",
+    "CompressionError",
+    "ContextCache",
+    "ContextCompressor",
+    "ContextEngine",
+    "ContextError",
+    "ContextNotFoundError",
+    "ContextPipeline",
+    "ContextPriority",
+    "ContextRanker",
+    "ContextSettings",
+    "ContextType",
+    "ConversationContext",
+    "DefaultAdapter",
+    "FormattingError",
+    "InvalidContextError",
+    "KnowledgeContext",
+    "MemoryContext",
+    "MemorySubtype",
+    "MessageRole",
+    "ModelAdapter",
+    "OpenAIAdapter",
+    "PipelineMetrics",
+    "PriorityScorer",
+    "RankingResult",
+    "RecencyScorer",
+    "RelevanceScorer",
+    "ScoredContext",
+    "ScoringError",
+    "SystemContext",
+    "TaskComplexity",
+    "TaskContext",
+    "TaskStatus",
+    "TokenBudget",
+    "TokenCalculator",
+    "TokenCountError",
+    "ToolContext",
+    "ToolResultStatus",
+    "TruncationResult",
+    "TruncationStrategy",
+    "create_context_engine",
+    "get_adapter",
+    "get_context_settings",
+    "get_default_settings",
+    "reset_context_settings",
+]

backend/app/services/context/adapters/__init__.py

@@ -0,0 +1,35 @@
+"""
+Model Adapters Module.
+
+Provides model-specific context formatting adapters.
+"""
+
+from .base import DefaultAdapter, ModelAdapter
+from .claude import ClaudeAdapter
+from .openai import OpenAIAdapter
+
+
+def get_adapter(model: str) -> ModelAdapter:
+    """
+    Get the appropriate adapter for a model.
+
+    Args:
+        model: Model name
+
+    Returns:
+        Adapter instance for the model
+    """
+    if ClaudeAdapter.matches_model(model):
+        return ClaudeAdapter()
+    elif OpenAIAdapter.matches_model(model):
+        return OpenAIAdapter()
+    return DefaultAdapter()
+
+
+__all__ = [
+    "ClaudeAdapter",
+    "DefaultAdapter",
+    "ModelAdapter",
+    "OpenAIAdapter",
+    "get_adapter",
+]

backend/app/services/context/adapters/base.py

@@ -0,0 +1,178 @@
+"""
+Base Model Adapter.
+
+Abstract base class for model-specific context formatting.
+"""
+
+from abc import ABC, abstractmethod
+from typing import Any, ClassVar
+
+from ..types import BaseContext, ContextType
+
+
+class ModelAdapter(ABC):
+    """
+    Abstract base adapter for model-specific context formatting.
+
+    Each adapter knows how to format contexts for optimal
+    understanding by a specific LLM family (Claude, OpenAI, etc.).
+    """
+
+    # Model name patterns this adapter handles
+    MODEL_PATTERNS: ClassVar[list[str]] = []
+
+    @classmethod
+    def matches_model(cls, model: str) -> bool:
+        """
+        Check if this adapter handles the given model.
+
+        Args:
+            model: Model name to check
+
+        Returns:
+            True if this adapter handles the model
+        """
+        model_lower = model.lower()
+        return any(pattern in model_lower for pattern in cls.MODEL_PATTERNS)
+
+    @abstractmethod
+    def format(
+        self,
+        contexts: list[BaseContext],
+        **kwargs: Any,
+    ) -> str:
+        """
+        Format contexts for the target model.
+
+        Args:
+            contexts: List of contexts to format
+            **kwargs: Additional formatting options
+
+        Returns:
+            Formatted context string
+        """
+        ...
+
+    @abstractmethod
+    def format_type(
+        self,
+        contexts: list[BaseContext],
+        context_type: ContextType,
+        **kwargs: Any,
+    ) -> str:
+        """
+        Format contexts of a specific type.
+
+        Args:
+            contexts: List of contexts of the same type
+            context_type: The type of contexts
+            **kwargs: Additional formatting options
+
+        Returns:
+            Formatted string for this context type
+        """
+        ...
+
+    def get_type_order(self) -> list[ContextType]:
+        """
+        Get the preferred order of context types.
+
+        Returns:
+            List of context types in preferred order
+        """
+        return [
+            ContextType.SYSTEM,
+            ContextType.TASK,
+            ContextType.KNOWLEDGE,
+            ContextType.CONVERSATION,
+            ContextType.TOOL,
+        ]
+
+    def group_by_type(
+        self, contexts: list[BaseContext]
+    ) -> dict[ContextType, list[BaseContext]]:
+        """
+        Group contexts by their type.
+
+        Args:
+            contexts: List of contexts to group
+
+        Returns:
+            Dictionary mapping context type to list of contexts
+        """
+        by_type: dict[ContextType, list[BaseContext]] = {}
+        for context in contexts:
+            ct = context.get_type()
+            if ct not in by_type:
+                by_type[ct] = []
+            by_type[ct].append(context)
+        return by_type
+
+    def get_separator(self) -> str:
+        """
+        Get the separator between context sections.
+
+        Returns:
+            Separator string
+        """
+        return "\n\n"
+
+
+class DefaultAdapter(ModelAdapter):
+    """
+    Default adapter for unknown models.
+
+    Uses simple plain-text formatting with minimal structure.
+    """
+
+    MODEL_PATTERNS: ClassVar[list[str]] = []  # Fallback adapter
+
+    @classmethod
+    def matches_model(cls, model: str) -> bool:
+        """Always returns True as fallback."""
+        return True
+
+    def format(
+        self,
+        contexts: list[BaseContext],
+        **kwargs: Any,
+    ) -> str:
+        """Format contexts as plain text."""
+        if not contexts:
+            return ""
+
+        by_type = self.group_by_type(contexts)
+        parts: list[str] = []
+
+        for ct in self.get_type_order():
+            if ct in by_type:
+                formatted = self.format_type(by_type[ct], ct, **kwargs)
+                if formatted:
+                    parts.append(formatted)
+
+        return self.get_separator().join(parts)
+
+    def format_type(
+        self,
+        contexts: list[BaseContext],
+        context_type: ContextType,
+        **kwargs: Any,
+    ) -> str:
+        """Format contexts of a type as plain text."""
+        if not contexts:
+            return ""
+
+        content = "\n\n".join(c.content for c in contexts)
+
+        if context_type == ContextType.SYSTEM:
+            return content
+        elif context_type == ContextType.TASK:
+            return f"Task:\n{content}"
+        elif context_type == ContextType.KNOWLEDGE:
+            return f"Reference Information:\n{content}"
+        elif context_type == ContextType.CONVERSATION:
+            return f"Previous Conversation:\n{content}"
+        elif context_type == ContextType.TOOL:
+            return f"Tool Results:\n{content}"
+
+        return content

backend/app/services/context/adapters/claude.py

@@ -0,0 +1,212 @@
+"""
+Claude Model Adapter.
+
+Provides Claude-specific context formatting using XML tags
+which Claude models understand natively.
+"""
+
+from typing import Any, ClassVar
+
+from ..types import BaseContext, ContextType
+from .base import ModelAdapter
+
+
+class ClaudeAdapter(ModelAdapter):
+    """
+    Claude-specific context formatting adapter.
+
+    Claude models have native understanding of XML structure,
+    so we use XML tags for clear delineation of context types.
+
+    Features:
+    - XML tags for each context type
+    - Document structure for knowledge contexts
+    - Role-based message formatting for conversations
+    - Tool result wrapping with tool names
+    """
+
+    MODEL_PATTERNS: ClassVar[list[str]] = ["claude", "anthropic"]
+
+    def format(
+        self,
+        contexts: list[BaseContext],
+        **kwargs: Any,
+    ) -> str:
+        """
+        Format contexts for Claude models.
+
+        Uses XML tags for structured content that Claude
+        understands natively.
+
+        Args:
+            contexts: List of contexts to format
+            **kwargs: Additional formatting options
+
+        Returns:
+            XML-structured context string
+        """
+        if not contexts:
+            return ""
+
+        by_type = self.group_by_type(contexts)
+        parts: list[str] = []
+
+        for ct in self.get_type_order():
+            if ct in by_type:
+                formatted = self.format_type(by_type[ct], ct, **kwargs)
+                if formatted:
+                    parts.append(formatted)
+
+        return self.get_separator().join(parts)
+
+    def format_type(
+        self,
+        contexts: list[BaseContext],
+        context_type: ContextType,
+        **kwargs: Any,
+    ) -> str:
+        """
+        Format contexts of a specific type for Claude.
+
+        Args:
+            contexts: List of contexts of the same type
+            context_type: The type of contexts
+            **kwargs: Additional formatting options
+
+        Returns:
+            XML-formatted string for this context type
+        """
+        if not contexts:
+            return ""
+
+        if context_type == ContextType.SYSTEM:
+            return self._format_system(contexts)
+        elif context_type == ContextType.TASK:
+            return self._format_task(contexts)
+        elif context_type == ContextType.KNOWLEDGE:
+            return self._format_knowledge(contexts)
+        elif context_type == ContextType.CONVERSATION:
+            return self._format_conversation(contexts)
+        elif context_type == ContextType.TOOL:
+            return self._format_tool(contexts)
+
+        # Fallback for any unhandled context types - still escape content
+        # to prevent XML injection if new types are added without updating adapter
+        return "\n".join(self._escape_xml_content(c.content) for c in contexts)
+
+    def _format_system(self, contexts: list[BaseContext]) -> str:
+        """Format system contexts."""
+        # System prompts are typically admin-controlled, but escape for safety
+        content = "\n\n".join(self._escape_xml_content(c.content) for c in contexts)
+        return f"<system_instructions>\n{content}\n</system_instructions>"
+
+    def _format_task(self, contexts: list[BaseContext]) -> str:
+        """Format task contexts."""
+        content = "\n\n".join(self._escape_xml_content(c.content) for c in contexts)
+        return f"<current_task>\n{content}\n</current_task>"
+
+    def _format_knowledge(self, contexts: list[BaseContext]) -> str:
+        """
+        Format knowledge contexts as structured documents.
+
+        Each knowledge context becomes a document with source attribution.
+        All content is XML-escaped to prevent injection attacks.
+        """
+        parts = ["<reference_documents>"]
+
+        for ctx in contexts:
+            source = self._escape_xml(ctx.source)
+            # Escape content to prevent XML injection
+            content = self._escape_xml_content(ctx.content)
+            score = ctx.metadata.get("score", ctx.metadata.get("relevance_score", ""))
+
+            if score:
+                # Escape score to prevent XML injection via metadata
+                escaped_score = self._escape_xml(str(score))
+                parts.append(
+                    f'<document source="{source}" relevance="{escaped_score}">'
+                )
+            else:
+                parts.append(f'<document source="{source}">')
+
+            parts.append(content)
+            parts.append("</document>")
+
+        parts.append("</reference_documents>")
+        return "\n".join(parts)
+
+    def _format_conversation(self, contexts: list[BaseContext]) -> str:
+        """
+        Format conversation contexts as message history.
+
+        Uses role-based message tags for clear turn delineation.
+        All content is XML-escaped to prevent prompt injection.
+        """
+        parts = ["<conversation_history>"]
+
+        for ctx in contexts:
+            role = self._escape_xml(ctx.metadata.get("role", "user"))
+            # Escape content to prevent prompt injection via fake XML tags
+            content = self._escape_xml_content(ctx.content)
+            parts.append(f'<message role="{role}">')
+            parts.append(content)
+            parts.append("</message>")
+
+        parts.append("</conversation_history>")
+        return "\n".join(parts)
+
+    def _format_tool(self, contexts: list[BaseContext]) -> str:
+        """
+        Format tool contexts as tool results.
+
+        Each tool result is wrapped with the tool name.
+        All content is XML-escaped to prevent injection.
+        """
+        parts = ["<tool_results>"]
+
+        for ctx in contexts:
+            tool_name = self._escape_xml(ctx.metadata.get("tool_name", "unknown"))
+            status = ctx.metadata.get("status", "")
+
+            if status:
+                parts.append(
+                    f'<tool_result name="{tool_name}" status="{self._escape_xml(status)}">'
+                )
+            else:
+                parts.append(f'<tool_result name="{tool_name}">')
+
+            # Escape content to prevent injection
+            parts.append(self._escape_xml_content(ctx.content))
+            parts.append("</tool_result>")
+
+        parts.append("</tool_results>")
+        return "\n".join(parts)
+
+    @staticmethod
+    def _escape_xml(text: str) -> str:
+        """Escape XML special characters in attribute values."""
+        return (
+            text.replace("&", "&amp;")
+            .replace("<", "&lt;")
+            .replace(">", "&gt;")
+            .replace('"', "&quot;")
+            .replace("'", "&apos;")
+        )
+
+    @staticmethod
+    def _escape_xml_content(text: str) -> str:
+        """
+        Escape XML special characters in element content.
+
+        This prevents XML injection attacks where malicious content
+        could break out of XML tags or inject fake tags for prompt injection.
+
+        Only escapes &, <, > since quotes don't need escaping in content.
+
+        Args:
+            text: Content text to escape
+
+        Returns:
+            XML-safe content string
+        """
+        return text.replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")

backend/app/services/context/adapters/openai.py

@@ -0,0 +1,160 @@
+"""
+OpenAI Model Adapter.
+
+Provides OpenAI-specific context formatting using markdown
+which GPT models understand well.
+"""
+
+from typing import Any, ClassVar
+
+from ..types import BaseContext, ContextType
+from .base import ModelAdapter
+
+
+class OpenAIAdapter(ModelAdapter):
+    """
+    OpenAI-specific context formatting adapter.
+
+    GPT models work well with markdown formatting,
+    so we use headers and structured markdown for clarity.
+
+    Features:
+    - Markdown headers for each context type
+    - Bulleted lists for document sources
+    - Bold role labels for conversations
+    - Code blocks for tool outputs
+    """
+
+    MODEL_PATTERNS: ClassVar[list[str]] = ["gpt", "openai", "o1", "o3"]
+
+    def format(
+        self,
+        contexts: list[BaseContext],
+        **kwargs: Any,
+    ) -> str:
+        """
+        Format contexts for OpenAI models.
+
+        Uses markdown formatting for structured content.
+
+        Args:
+            contexts: List of contexts to format
+            **kwargs: Additional formatting options
+
+        Returns:
+            Markdown-structured context string
+        """
+        if not contexts:
+            return ""
+
+        by_type = self.group_by_type(contexts)
+        parts: list[str] = []
+
+        for ct in self.get_type_order():
+            if ct in by_type:
+                formatted = self.format_type(by_type[ct], ct, **kwargs)
+                if formatted:
+                    parts.append(formatted)
+
+        return self.get_separator().join(parts)
+
+    def format_type(
+        self,
+        contexts: list[BaseContext],
+        context_type: ContextType,
+        **kwargs: Any,
+    ) -> str:
+        """
+        Format contexts of a specific type for OpenAI.
+
+        Args:
+            contexts: List of contexts of the same type
+            context_type: The type of contexts
+            **kwargs: Additional formatting options
+
+        Returns:
+            Markdown-formatted string for this context type
+        """
+        if not contexts:
+            return ""
+
+        if context_type == ContextType.SYSTEM:
+            return self._format_system(contexts)
+        elif context_type == ContextType.TASK:
+            return self._format_task(contexts)
+        elif context_type == ContextType.KNOWLEDGE:
+            return self._format_knowledge(contexts)
+        elif context_type == ContextType.CONVERSATION:
+            return self._format_conversation(contexts)
+        elif context_type == ContextType.TOOL:
+            return self._format_tool(contexts)
+
+        return "\n".join(c.content for c in contexts)
+
+    def _format_system(self, contexts: list[BaseContext]) -> str:
+        """Format system contexts."""
+        content = "\n\n".join(c.content for c in contexts)
+        return content
+
+    def _format_task(self, contexts: list[BaseContext]) -> str:
+        """Format task contexts."""
+        content = "\n\n".join(c.content for c in contexts)
+        return f"## Current Task\n\n{content}"
+
+    def _format_knowledge(self, contexts: list[BaseContext]) -> str:
+        """
+        Format knowledge contexts as structured documents.
+
+        Each knowledge context becomes a section with source attribution.
+        """
+        parts = ["## Reference Documents\n"]
+
+        for ctx in contexts:
+            source = ctx.source
+            score = ctx.metadata.get("score", ctx.metadata.get("relevance_score", ""))
+
+            if score:
+                parts.append(f"### Source: {source} (relevance: {score})\n")
+            else:
+                parts.append(f"### Source: {source}\n")
+
+            parts.append(ctx.content)
+            parts.append("")
+
+        return "\n".join(parts)
+
+    def _format_conversation(self, contexts: list[BaseContext]) -> str:
+        """
+        Format conversation contexts as message history.
+
+        Uses bold role labels for clear turn delineation.
+        """
+        parts = []
+
+        for ctx in contexts:
+            role = ctx.metadata.get("role", "user").upper()
+            parts.append(f"**{role}**: {ctx.content}")
+
+        return "\n\n".join(parts)
+
+    def _format_tool(self, contexts: list[BaseContext]) -> str:
+        """
+        Format tool contexts as tool results.
+
+        Each tool result is in a code block with the tool name.
+        """
+        parts = ["## Recent Tool Results\n"]
+
+        for ctx in contexts:
+            tool_name = ctx.metadata.get("tool_name", "unknown")
+            status = ctx.metadata.get("status", "")
+
+            if status:
+                parts.append(f"### Tool: {tool_name} ({status})\n")
+            else:
+                parts.append(f"### Tool: {tool_name}\n")
+
+            parts.append(f"```\n{ctx.content}\n```")
+            parts.append("")
+
+        return "\n".join(parts)

backend/app/services/context/assembly/__init__.py

@@ -0,0 +1,12 @@
+"""
+Context Assembly Module.
+
+Provides the assembly pipeline and formatting.
+"""
+
+from .pipeline import ContextPipeline, PipelineMetrics
+
+__all__ = [
+    "ContextPipeline",
+    "PipelineMetrics",
+]

backend/app/services/context/assembly/pipeline.py

@@ -0,0 +1,362 @@
+"""
+Context Assembly Pipeline.
+
+Orchestrates the full context assembly workflow:
+Gather → Count → Score → Rank → Compress → Format
+"""
+
+import asyncio
+import logging
+import time
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from typing import TYPE_CHECKING, Any
+
+from ..adapters import get_adapter
+from ..budget import BudgetAllocator, TokenBudget, TokenCalculator
+from ..compression.truncation import ContextCompressor
+from ..config import ContextSettings, get_context_settings
+from ..exceptions import AssemblyTimeoutError
+from ..prioritization import ContextRanker
+from ..scoring import CompositeScorer
+from ..types import AssembledContext, BaseContext, ContextType
+
+if TYPE_CHECKING:
+    from app.services.mcp.client_manager import MCPClientManager
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class PipelineMetrics:
+    """Metrics from pipeline execution."""
+
+    start_time: datetime = field(default_factory=lambda: datetime.now(UTC))
+    end_time: datetime | None = None
+    total_contexts: int = 0
+    selected_contexts: int = 0
+    excluded_contexts: int = 0
+    compressed_contexts: int = 0
+    total_tokens: int = 0
+    assembly_time_ms: float = 0.0
+    scoring_time_ms: float = 0.0
+    compression_time_ms: float = 0.0
+    formatting_time_ms: float = 0.0
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "start_time": self.start_time.isoformat(),
+            "end_time": self.end_time.isoformat() if self.end_time else None,
+            "total_contexts": self.total_contexts,
+            "selected_contexts": self.selected_contexts,
+            "excluded_contexts": self.excluded_contexts,
+            "compressed_contexts": self.compressed_contexts,
+            "total_tokens": self.total_tokens,
+            "assembly_time_ms": round(self.assembly_time_ms, 2),
+            "scoring_time_ms": round(self.scoring_time_ms, 2),
+            "compression_time_ms": round(self.compression_time_ms, 2),
+            "formatting_time_ms": round(self.formatting_time_ms, 2),
+        }
+
+
+class ContextPipeline:
+    """
+    Context assembly pipeline.
+
+    Orchestrates the full workflow of context assembly:
+    1. Validate and count tokens for all contexts
+    2. Score contexts based on relevance, recency, and priority
+    3. Rank and select contexts within budget
+    4. Compress if needed to fit remaining budget
+    5. Format for the target model
+    """
+
+    def __init__(
+        self,
+        mcp_manager: "MCPClientManager | None" = None,
+        settings: ContextSettings | None = None,
+        calculator: TokenCalculator | None = None,
+        scorer: CompositeScorer | None = None,
+        ranker: ContextRanker | None = None,
+        compressor: ContextCompressor | None = None,
+    ) -> None:
+        """
+        Initialize the context pipeline.
+
+        Args:
+            mcp_manager: MCP client manager for LLM Gateway integration
+            settings: Context settings
+            calculator: Token calculator
+            scorer: Context scorer
+            ranker: Context ranker
+            compressor: Context compressor
+        """
+        self._settings = settings or get_context_settings()
+        self._mcp = mcp_manager
+
+        # Initialize components
+        self._calculator = calculator or TokenCalculator(mcp_manager=mcp_manager)
+        self._scorer = scorer or CompositeScorer(
+            mcp_manager=mcp_manager, settings=self._settings
+        )
+        self._ranker = ranker or ContextRanker(
+            scorer=self._scorer, calculator=self._calculator
+        )
+        self._compressor = compressor or ContextCompressor(calculator=self._calculator)
+        self._allocator = BudgetAllocator(self._settings)
+
+    def set_mcp_manager(self, mcp_manager: "MCPClientManager") -> None:
+        """Set MCP manager for all components."""
+        self._mcp = mcp_manager
+        self._calculator.set_mcp_manager(mcp_manager)
+        self._scorer.set_mcp_manager(mcp_manager)
+
+    async def assemble(
+        self,
+        contexts: list[BaseContext],
+        query: str,
+        model: str,
+        max_tokens: int | None = None,
+        custom_budget: TokenBudget | None = None,
+        compress: bool = True,
+        format_output: bool = True,
+        timeout_ms: int | None = None,
+    ) -> AssembledContext:
+        """
+        Assemble context for an LLM request.
+
+        This is the main entry point for context assembly.
+
+        Args:
+            contexts: List of contexts to assemble
+            query: Query to optimize for
+            model: Target model name
+            max_tokens: Maximum total tokens (uses model default if None)
+            custom_budget: Optional pre-configured budget
+            compress: Whether to compress oversized contexts
+            format_output: Whether to format the final output
+            timeout_ms: Maximum assembly time in milliseconds
+
+        Returns:
+            AssembledContext with optimized content
+
+        Raises:
+            AssemblyTimeoutError: If assembly exceeds timeout
+        """
+        timeout = timeout_ms or self._settings.max_assembly_time_ms
+        start = time.perf_counter()
+        metrics = PipelineMetrics(total_contexts=len(contexts))
+
+        try:
+            # Create or use budget
+            if custom_budget:
+                budget = custom_budget
+            elif max_tokens:
+                budget = self._allocator.create_budget(max_tokens)
+            else:
+                budget = self._allocator.create_budget_for_model(model)
+
+            # 1. Count tokens for all contexts (with timeout enforcement)
+            try:
+                await asyncio.wait_for(
+                    self._ensure_token_counts(contexts, model),
+                    timeout=self._remaining_timeout(start, timeout),
+                )
+            except TimeoutError:
+                elapsed_ms = (time.perf_counter() - start) * 1000
+                raise AssemblyTimeoutError(
+                    message="Context assembly timed out during token counting",
+                    elapsed_ms=elapsed_ms,
+                    timeout_ms=timeout,
+                )
+
+            # Check timeout (handles edge case where operation finished just at limit)
+            self._check_timeout(start, timeout, "token counting")
+
+            # 2. Score and rank contexts (with timeout enforcement)
+            scoring_start = time.perf_counter()
+            try:
+                ranking_result = await asyncio.wait_for(
+                    self._ranker.rank(
+                        contexts=contexts,
+                        query=query,
+                        budget=budget,
+                        model=model,
+                    ),
+                    timeout=self._remaining_timeout(start, timeout),
+                )
+            except TimeoutError:
+                elapsed_ms = (time.perf_counter() - start) * 1000
+                raise AssemblyTimeoutError(
+                    message="Context assembly timed out during scoring/ranking",
+                    elapsed_ms=elapsed_ms,
+                    timeout_ms=timeout,
+                )
+            metrics.scoring_time_ms = (time.perf_counter() - scoring_start) * 1000
+
+            selected_contexts = ranking_result.selected_contexts
+            metrics.selected_contexts = len(selected_contexts)
+            metrics.excluded_contexts = len(ranking_result.excluded)
+
+            # Check timeout
+            self._check_timeout(start, timeout, "scoring")
+
+            # 3. Compress if needed and enabled (with timeout enforcement)
+            if compress and self._needs_compression(selected_contexts, budget):
+                compression_start = time.perf_counter()
+                try:
+                    selected_contexts = await asyncio.wait_for(
+                        self._compressor.compress_contexts(
+                            selected_contexts, budget, model
+                        ),
+                        timeout=self._remaining_timeout(start, timeout),
+                    )
+                except TimeoutError:
+                    elapsed_ms = (time.perf_counter() - start) * 1000
+                    raise AssemblyTimeoutError(
+                        message="Context assembly timed out during compression",
+                        elapsed_ms=elapsed_ms,
+                        timeout_ms=timeout,
+                    )
+                metrics.compression_time_ms = (
+                    time.perf_counter() - compression_start
+                ) * 1000
+                metrics.compressed_contexts = sum(
+                    1 for c in selected_contexts if c.metadata.get("truncated", False)
+                )
+
+            # Check timeout
+            self._check_timeout(start, timeout, "compression")
+
+            # 4. Format output
+            formatting_start = time.perf_counter()
+            if format_output:
+                formatted_content = self._format_contexts(selected_contexts, model)
+            else:
+                formatted_content = "\n\n".join(c.content for c in selected_contexts)
+            metrics.formatting_time_ms = (time.perf_counter() - formatting_start) * 1000
+
+            # Calculate final metrics
+            total_tokens = sum(c.token_count or 0 for c in selected_contexts)
+            metrics.total_tokens = total_tokens
+            metrics.assembly_time_ms = (time.perf_counter() - start) * 1000
+            metrics.end_time = datetime.now(UTC)
+
+            return AssembledContext(
+                content=formatted_content,
+                total_tokens=total_tokens,
+                context_count=len(selected_contexts),
+                assembly_time_ms=metrics.assembly_time_ms,
+                model=model,
+                contexts=selected_contexts,
+                excluded_count=metrics.excluded_contexts,
+                metadata={
+                    "metrics": metrics.to_dict(),
+                    "query": query,
+                    "budget": budget.to_dict(),
+                },
+            )
+
+        except AssemblyTimeoutError:
+            raise
+        except Exception as e:
+            logger.error(f"Context assembly failed: {e}", exc_info=True)
+            raise
+
+    async def _ensure_token_counts(
+        self,
+        contexts: list[BaseContext],
+        model: str | None = None,
+    ) -> None:
+        """Ensure all contexts have token counts."""
+        tasks = []
+        for context in contexts:
+            if context.token_count is None:
+                tasks.append(self._count_and_set(context, model))
+
+        if tasks:
+            await asyncio.gather(*tasks)
+
+    async def _count_and_set(
+        self,
+        context: BaseContext,
+        model: str | None = None,
+    ) -> None:
+        """Count tokens and set on context."""
+        count = await self._calculator.count_tokens(context.content, model)
+        context.token_count = count
+
+    def _needs_compression(
+        self,
+        contexts: list[BaseContext],
+        budget: TokenBudget,
+    ) -> bool:
+        """Check if any contexts exceed their type budget."""
+        # Group by type and check totals
+        by_type: dict[ContextType, int] = {}
+        for context in contexts:
+            ct = context.get_type()
+            by_type[ct] = by_type.get(ct, 0) + (context.token_count or 0)
+
+        for ct, total in by_type.items():
+            if total > budget.get_allocation(ct):
+                return True
+
+        # Also check if utilization exceeds threshold
+        return budget.utilization() > self._settings.compression_threshold
+
+    def _format_contexts(
+        self,
+        contexts: list[BaseContext],
+        model: str,
+    ) -> str:
+        """
+        Format contexts for the target model.
+
+        Uses model-specific adapters (ClaudeAdapter, OpenAIAdapter, etc.)
+        to format contexts optimally for each model family.
+
+        Args:
+            contexts: Contexts to format
+            model: Target model name
+
+        Returns:
+            Formatted context string
+        """
+        adapter = get_adapter(model)
+        return adapter.format(contexts)
+
+    def _check_timeout(
+        self,
+        start: float,
+        timeout_ms: int,
+        phase: str,
+    ) -> None:
+        """Check if timeout exceeded and raise if so."""
+        elapsed_ms = (time.perf_counter() - start) * 1000
+        if elapsed_ms >= timeout_ms:
+            raise AssemblyTimeoutError(
+                message=f"Context assembly timed out during {phase}",
+                elapsed_ms=elapsed_ms,
+                timeout_ms=timeout_ms,
+            )
+
+    def _remaining_timeout(self, start: float, timeout_ms: int) -> float:
+        """
+        Calculate remaining timeout in seconds for asyncio.wait_for.
+
+        Returns at least a small positive value to avoid immediate timeout
+        edge cases with wait_for.
+
+        Args:
+            start: Start time from time.perf_counter()
+            timeout_ms: Total timeout in milliseconds
+
+        Returns:
+            Remaining timeout in seconds (minimum 0.001)
+        """
+        elapsed_ms = (time.perf_counter() - start) * 1000
+        remaining_ms = timeout_ms - elapsed_ms
+        # Return at least 1ms to avoid zero/negative timeout edge cases
+        return max(remaining_ms / 1000.0, 0.001)

backend/app/services/context/budget/__init__.py

@@ -0,0 +1,14 @@
+"""
+Token Budget Management Module.
+
+Provides token counting and budget allocation.
+"""
+
+from .allocator import BudgetAllocator, TokenBudget
+from .calculator import TokenCalculator
+
+__all__ = [
+    "BudgetAllocator",
+    "TokenBudget",
+    "TokenCalculator",
+]

backend/app/services/context/budget/allocator.py

@@ -0,0 +1,444 @@
+"""
+Token Budget Allocator for Context Management.
+
+Manages token budget allocation across context types.
+"""
+
+from dataclasses import dataclass, field
+from typing import Any
+
+from ..config import ContextSettings, get_context_settings
+from ..exceptions import BudgetExceededError
+from ..types import ContextType
+
+
+@dataclass
+class TokenBudget:
+    """
+    Token budget allocation and tracking.
+
+    Tracks allocated tokens per context type and
+    monitors usage to prevent overflows.
+    """
+
+    # Total budget
+    total: int
+
+    # Allocated per type
+    system: int = 0
+    task: int = 0
+    knowledge: int = 0
+    conversation: int = 0
+    tools: int = 0
+    memory: int = 0  # Agent memory (working, episodic, semantic, procedural)
+    response_reserve: int = 0
+    buffer: int = 0
+
+    # Usage tracking
+    used: dict[str, int] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        """Initialize usage tracking."""
+        if not self.used:
+            self.used = {ct.value: 0 for ct in ContextType}
+
+    def get_allocation(self, context_type: ContextType | str) -> int:
+        """
+        Get allocated tokens for a context type.
+
+        Args:
+            context_type: Context type to get allocation for
+
+        Returns:
+            Allocated token count
+        """
+        if isinstance(context_type, ContextType):
+            context_type = context_type.value
+
+        allocation_map = {
+            "system": self.system,
+            "task": self.task,
+            "knowledge": self.knowledge,
+            "conversation": self.conversation,
+            "tool": self.tools,
+            "memory": self.memory,
+        }
+        return allocation_map.get(context_type, 0)
+
+    def get_used(self, context_type: ContextType | str) -> int:
+        """
+        Get used tokens for a context type.
+
+        Args:
+            context_type: Context type to check
+
+        Returns:
+            Used token count
+        """
+        if isinstance(context_type, ContextType):
+            context_type = context_type.value
+        return self.used.get(context_type, 0)
+
+    def remaining(self, context_type: ContextType | str) -> int:
+        """
+        Get remaining tokens for a context type.
+
+        Args:
+            context_type: Context type to check
+
+        Returns:
+            Remaining token count
+        """
+        allocated = self.get_allocation(context_type)
+        used = self.get_used(context_type)
+        return max(0, allocated - used)
+
+    def total_remaining(self) -> int:
+        """
+        Get total remaining tokens across all types.
+
+        Returns:
+            Total remaining tokens
+        """
+        total_used = sum(self.used.values())
+        usable = self.total - self.response_reserve - self.buffer
+        return max(0, usable - total_used)
+
+    def total_used(self) -> int:
+        """
+        Get total used tokens.
+
+        Returns:
+            Total used tokens
+        """
+        return sum(self.used.values())
+
+    def can_fit(self, context_type: ContextType | str, tokens: int) -> bool:
+        """
+        Check if tokens fit within budget for a type.
+
+        Args:
+            context_type: Context type to check
+            tokens: Number of tokens to fit
+
+        Returns:
+            True if tokens fit within remaining budget
+        """
+        return tokens <= self.remaining(context_type)
+
+    def allocate(
+        self,
+        context_type: ContextType | str,
+        tokens: int,
+        force: bool = False,
+    ) -> bool:
+        """
+        Allocate (use) tokens from a context type's budget.
+
+        Args:
+            context_type: Context type to allocate from
+            tokens: Number of tokens to allocate
+            force: If True, allow exceeding budget
+
+        Returns:
+            True if allocation succeeded
+
+        Raises:
+            BudgetExceededError: If tokens exceed budget and force=False
+        """
+        if isinstance(context_type, ContextType):
+            context_type = context_type.value
+
+        if not force and not self.can_fit(context_type, tokens):
+            raise BudgetExceededError(
+                message=f"Token budget exceeded for {context_type}",
+                allocated=self.get_allocation(context_type),
+                requested=self.get_used(context_type) + tokens,
+                context_type=context_type,
+            )
+
+        self.used[context_type] = self.used.get(context_type, 0) + tokens
+        return True
+
+    def deallocate(
+        self,
+        context_type: ContextType | str,
+        tokens: int,
+    ) -> None:
+        """
+        Deallocate (return) tokens to a context type's budget.
+
+        Args:
+            context_type: Context type to return to
+            tokens: Number of tokens to return
+        """
+        if isinstance(context_type, ContextType):
+            context_type = context_type.value
+
+        current = self.used.get(context_type, 0)
+        self.used[context_type] = max(0, current - tokens)
+
+    def reset(self) -> None:
+        """Reset all usage tracking."""
+        self.used = {ct.value: 0 for ct in ContextType}
+
+    def utilization(self, context_type: ContextType | str | None = None) -> float:
+        """
+        Get budget utilization percentage.
+
+        Args:
+            context_type: Specific type or None for total
+
+        Returns:
+            Utilization as a fraction (0.0 to 1.0+)
+        """
+        if context_type is None:
+            usable = self.total - self.response_reserve - self.buffer
+            if usable <= 0:
+                return 0.0
+            return self.total_used() / usable
+
+        allocated = self.get_allocation(context_type)
+        if allocated <= 0:
+            return 0.0
+        return self.get_used(context_type) / allocated
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert budget to dictionary."""
+        return {
+            "total": self.total,
+            "allocations": {
+                "system": self.system,
+                "task": self.task,
+                "knowledge": self.knowledge,
+                "conversation": self.conversation,
+                "tools": self.tools,
+                "memory": self.memory,
+                "response_reserve": self.response_reserve,
+                "buffer": self.buffer,
+            },
+            "used": dict(self.used),
+            "remaining": {ct.value: self.remaining(ct) for ct in ContextType},
+            "total_used": self.total_used(),
+            "total_remaining": self.total_remaining(),
+            "utilization": round(self.utilization(), 3),
+        }
+
+
+class BudgetAllocator:
+    """
+    Budget allocator for context management.
+
+    Creates token budgets based on configuration and
+    model context window sizes.
+    """
+
+    def __init__(self, settings: ContextSettings | None = None) -> None:
+        """
+        Initialize budget allocator.
+
+        Args:
+            settings: Context settings (uses default if None)
+        """
+        self._settings = settings or get_context_settings()
+
+    def create_budget(
+        self,
+        total_tokens: int,
+        custom_allocations: dict[str, float] | None = None,
+    ) -> TokenBudget:
+        """
+        Create a token budget with allocations.
+
+        Args:
+            total_tokens: Total available tokens
+            custom_allocations: Optional custom allocation percentages
+
+        Returns:
+            TokenBudget with allocations set
+        """
+        # Use custom or default allocations
+        if custom_allocations:
+            alloc = custom_allocations
+        else:
+            alloc = self._settings.get_budget_allocation()
+
+        return TokenBudget(
+            total=total_tokens,
+            system=int(total_tokens * alloc.get("system", 0.05)),
+            task=int(total_tokens * alloc.get("task", 0.10)),
+            knowledge=int(total_tokens * alloc.get("knowledge", 0.30)),
+            conversation=int(total_tokens * alloc.get("conversation", 0.15)),
+            tools=int(total_tokens * alloc.get("tools", 0.05)),
+            memory=int(total_tokens * alloc.get("memory", 0.15)),
+            response_reserve=int(total_tokens * alloc.get("response", 0.15)),
+            buffer=int(total_tokens * alloc.get("buffer", 0.05)),
+        )
+
+    def adjust_budget(
+        self,
+        budget: TokenBudget,
+        context_type: ContextType | str,
+        adjustment: int,
+    ) -> TokenBudget:
+        """
+        Adjust a specific allocation in a budget.
+
+        Takes tokens from buffer and adds to specified type.
+
+        Args:
+            budget: Budget to adjust
+            context_type: Type to adjust
+            adjustment: Positive to increase, negative to decrease
+
+        Returns:
+            Adjusted budget
+        """
+        if isinstance(context_type, ContextType):
+            context_type = context_type.value
+
+        # Calculate adjustment (limited by buffer for increases, by current allocation for decreases)
+        if adjustment > 0:
+            # Taking from buffer - limited by available buffer
+            actual_adjustment = min(adjustment, budget.buffer)
+            budget.buffer -= actual_adjustment
+        else:
+            # Returning to buffer - limited by current allocation of target type
+            current_allocation = budget.get_allocation(context_type)
+            # Can't return more than current allocation
+            actual_adjustment = max(adjustment, -current_allocation)
+            # Add returned tokens back to buffer (adjustment is negative, so subtract)
+            budget.buffer -= actual_adjustment
+
+        # Apply to target type
+        if context_type == "system":
+            budget.system = max(0, budget.system + actual_adjustment)
+        elif context_type == "task":
+            budget.task = max(0, budget.task + actual_adjustment)
+        elif context_type == "knowledge":
+            budget.knowledge = max(0, budget.knowledge + actual_adjustment)
+        elif context_type == "conversation":
+            budget.conversation = max(0, budget.conversation + actual_adjustment)
+        elif context_type == "tool":
+            budget.tools = max(0, budget.tools + actual_adjustment)
+        elif context_type == "memory":
+            budget.memory = max(0, budget.memory + actual_adjustment)
+
+        return budget
+
+    def rebalance_budget(
+        self,
+        budget: TokenBudget,
+        prioritize: list[ContextType] | None = None,
+    ) -> TokenBudget:
+        """
+        Rebalance budget based on actual usage.
+
+        Moves unused allocations to prioritized types.
+
+        Args:
+            budget: Budget to rebalance
+            prioritize: Types to prioritize (in order)
+
+        Returns:
+            Rebalanced budget
+        """
+        if prioritize is None:
+            prioritize = [
+                ContextType.KNOWLEDGE,
+                ContextType.MEMORY,
+                ContextType.TASK,
+                ContextType.SYSTEM,
+            ]
+
+        # Calculate unused tokens per type
+        unused: dict[str, int] = {}
+        for ct in ContextType:
+            remaining = budget.remaining(ct)
+            if remaining > 0:
+                unused[ct.value] = remaining
+
+        # Calculate total reclaimable (excluding prioritized types)
+        prioritize_values = {ct.value for ct in prioritize}
+        reclaimable = sum(
+            tokens for ct, tokens in unused.items() if ct not in prioritize_values
+        )
+
+        # Redistribute to prioritized types that are near capacity
+        for ct in prioritize:
+            utilization = budget.utilization(ct)
+
+            if utilization > 0.8:  # Near capacity
+                # Give more tokens from reclaimable pool
+                bonus = min(reclaimable, budget.get_allocation(ct) // 2)
+                self.adjust_budget(budget, ct, bonus)
+                reclaimable -= bonus
+
+            if reclaimable <= 0:
+                break
+
+        return budget
+
+    def get_model_context_size(self, model: str) -> int:
+        """
+        Get context window size for a model.
+
+        Args:
+            model: Model name
+
+        Returns:
+            Context window size in tokens
+        """
+        # Common model context sizes
+        context_sizes = {
+            "claude-3-opus": 200000,
+            "claude-3-sonnet": 200000,
+            "claude-3-haiku": 200000,
+            "claude-3-5-sonnet": 200000,
+            "claude-3-5-haiku": 200000,
+            "claude-opus-4": 200000,
+            "gpt-4-turbo": 128000,
+            "gpt-4": 8192,
+            "gpt-4-32k": 32768,
+            "gpt-4o": 128000,
+            "gpt-4o-mini": 128000,
+            "gpt-3.5-turbo": 16385,
+            "gemini-1.5-pro": 2000000,
+            "gemini-1.5-flash": 1000000,
+            "gemini-2.0-flash": 1000000,
+            "qwen-plus": 32000,
+            "qwen-turbo": 8000,
+            "deepseek-chat": 64000,
+            "deepseek-reasoner": 64000,
+        }
+
+        # Check exact match first
+        model_lower = model.lower()
+        if model_lower in context_sizes:
+            return context_sizes[model_lower]
+
+        # Check prefix match
+        for model_name, size in context_sizes.items():
+            if model_lower.startswith(model_name):
+                return size
+
+        # Default fallback
+        return 8192
+
+    def create_budget_for_model(
+        self,
+        model: str,
+        custom_allocations: dict[str, float] | None = None,
+    ) -> TokenBudget:
+        """
+        Create a budget based on model's context window.
+
+        Args:
+            model: Model name
+            custom_allocations: Optional custom allocation percentages
+
+        Returns:
+            TokenBudget sized for the model
+        """
+        context_size = self.get_model_context_size(model)
+        return self.create_budget(context_size, custom_allocations)

backend/app/services/context/budget/calculator.py

@@ -0,0 +1,285 @@
+"""
+Token Calculator for Context Management.
+
+Provides token counting with caching and fallback estimation.
+Integrates with LLM Gateway for accurate counts.
+"""
+
+import hashlib
+import logging
+from typing import TYPE_CHECKING, Any, ClassVar, Protocol
+
+if TYPE_CHECKING:
+    from app.services.mcp.client_manager import MCPClientManager
+
+logger = logging.getLogger(__name__)
+
+
+class TokenCounterProtocol(Protocol):
+    """Protocol for token counting implementations."""
+
+    async def count_tokens(
+        self,
+        text: str,
+        model: str | None = None,
+    ) -> int:
+        """Count tokens in text."""
+        ...
+
+
+class TokenCalculator:
+    """
+    Token calculator with LLM Gateway integration.
+
+    Features:
+    - In-memory caching for repeated text
+    - Fallback to character-based estimation
+    - Model-specific counting when possible
+
+    The calculator uses the LLM Gateway's count_tokens tool
+    for accurate counting, with a local cache to avoid
+    repeated calls for the same content.
+    """
+
+    # Default characters per token ratio for estimation
+    DEFAULT_CHARS_PER_TOKEN: ClassVar[float] = 4.0
+
+    # Model-specific ratios (more accurate estimation)
+    MODEL_CHAR_RATIOS: ClassVar[dict[str, float]] = {
+        "claude": 3.5,
+        "gpt-4": 4.0,
+        "gpt-3.5": 4.0,
+        "gemini": 4.0,
+    }
+
+    def __init__(
+        self,
+        mcp_manager: "MCPClientManager | None" = None,
+        project_id: str = "system",
+        agent_id: str = "context-engine",
+        cache_enabled: bool = True,
+        cache_max_size: int = 10000,
+    ) -> None:
+        """
+        Initialize token calculator.
+
+        Args:
+            mcp_manager: MCP client manager for LLM Gateway calls
+            project_id: Project ID for LLM Gateway calls
+            agent_id: Agent ID for LLM Gateway calls
+            cache_enabled: Whether to enable in-memory caching
+            cache_max_size: Maximum cache entries
+        """
+        self._mcp = mcp_manager
+        self._project_id = project_id
+        self._agent_id = agent_id
+        self._cache_enabled = cache_enabled
+        self._cache_max_size = cache_max_size
+
+        # In-memory cache: hash(model:text) -> token_count
+        self._cache: dict[str, int] = {}
+        self._cache_hits = 0
+        self._cache_misses = 0
+
+    def _get_cache_key(self, text: str, model: str | None) -> str:
+        """Generate cache key from text and model."""
+        # Use hash for efficient storage
+        content = f"{model or 'default'}:{text}"
+        return hashlib.sha256(content.encode()).hexdigest()[:32]
+
+    def _check_cache(self, cache_key: str) -> int | None:
+        """Check cache for existing count."""
+        if not self._cache_enabled:
+            return None
+
+        if cache_key in self._cache:
+            self._cache_hits += 1
+            return self._cache[cache_key]
+
+        self._cache_misses += 1
+        return None
+
+    def _store_cache(self, cache_key: str, count: int) -> None:
+        """Store count in cache."""
+        if not self._cache_enabled:
+            return
+
+        # Simple LRU-like eviction: remove oldest entries when full
+        if len(self._cache) >= self._cache_max_size:
+            # Remove first 10% of entries
+            entries_to_remove = self._cache_max_size // 10
+            keys_to_remove = list(self._cache.keys())[:entries_to_remove]
+            for key in keys_to_remove:
+                del self._cache[key]
+
+        self._cache[cache_key] = count
+
+    def estimate_tokens(self, text: str, model: str | None = None) -> int:
+        """
+        Estimate token count based on character count.
+
+        This is a fast fallback when LLM Gateway is unavailable.
+
+        Args:
+            text: Text to count
+            model: Optional model for more accurate ratio
+
+        Returns:
+            Estimated token count
+        """
+        if not text:
+            return 0
+
+        # Get model-specific ratio
+        ratio = self.DEFAULT_CHARS_PER_TOKEN
+        if model:
+            model_lower = model.lower()
+            for model_prefix, model_ratio in self.MODEL_CHAR_RATIOS.items():
+                if model_prefix in model_lower:
+                    ratio = model_ratio
+                    break
+
+        return max(1, int(len(text) / ratio))
+
+    async def count_tokens(
+        self,
+        text: str,
+        model: str | None = None,
+    ) -> int:
+        """
+        Count tokens in text.
+
+        Uses LLM Gateway for accurate counts with fallback to estimation.
+
+        Args:
+            text: Text to count
+            model: Optional model for accurate counting
+
+        Returns:
+            Token count
+        """
+        if not text:
+            return 0
+
+        # Check cache first
+        cache_key = self._get_cache_key(text, model)
+        cached = self._check_cache(cache_key)
+        if cached is not None:
+            return cached
+
+        # Try LLM Gateway
+        if self._mcp is not None:
+            try:
+                result = await self._mcp.call_tool(
+                    server="llm-gateway",
+                    tool="count_tokens",
+                    args={
+                        "project_id": self._project_id,
+                        "agent_id": self._agent_id,
+                        "text": text,
+                        "model": model,
+                    },
+                )
+
+                # Parse result
+                if result.success and result.data:
+                    count = self._parse_token_count(result.data)
+                    if count is not None:
+                        self._store_cache(cache_key, count)
+                        return count
+
+            except Exception as e:
+                logger.warning(f"LLM Gateway token count failed, using estimation: {e}")
+
+        # Fallback to estimation
+        count = self.estimate_tokens(text, model)
+        self._store_cache(cache_key, count)
+        return count
+
+    def _parse_token_count(self, data: Any) -> int | None:
+        """Parse token count from LLM Gateway response."""
+        if isinstance(data, dict):
+            if "token_count" in data:
+                return int(data["token_count"])
+            if "tokens" in data:
+                return int(data["tokens"])
+            if "count" in data:
+                return int(data["count"])
+
+        if isinstance(data, int):
+            return data
+
+        if isinstance(data, str):
+            # Try to parse from text content
+            try:
+                # Handle {"token_count": 123} or just "123"
+                import json
+
+                parsed = json.loads(data)
+                if isinstance(parsed, dict) and "token_count" in parsed:
+                    return int(parsed["token_count"])
+                if isinstance(parsed, int):
+                    return parsed
+            except (json.JSONDecodeError, ValueError):
+                # Try direct int conversion
+                try:
+                    return int(data)
+                except ValueError:
+                    pass
+
+        return None
+
+    async def count_tokens_batch(
+        self,
+        texts: list[str],
+        model: str | None = None,
+    ) -> list[int]:
+        """
+        Count tokens for multiple texts.
+
+        Efficient batch counting with caching and parallel execution.
+
+        Args:
+            texts: List of texts to count
+            model: Optional model for accurate counting
+
+        Returns:
+            List of token counts (same order as input)
+        """
+        import asyncio
+
+        if not texts:
+            return []
+
+        # Execute all token counts in parallel for better performance
+        tasks = [self.count_tokens(text, model) for text in texts]
+        return await asyncio.gather(*tasks)
+
+    def clear_cache(self) -> None:
+        """Clear the token count cache."""
+        self._cache.clear()
+        self._cache_hits = 0
+        self._cache_misses = 0
+
+    def get_cache_stats(self) -> dict[str, Any]:
+        """Get cache statistics."""
+        total = self._cache_hits + self._cache_misses
+        hit_rate = self._cache_hits / total if total > 0 else 0.0
+
+        return {
+            "enabled": self._cache_enabled,
+            "size": len(self._cache),
+            "max_size": self._cache_max_size,
+            "hits": self._cache_hits,
+            "misses": self._cache_misses,
+            "hit_rate": round(hit_rate, 3),
+        }
+
+    def set_mcp_manager(self, mcp_manager: "MCPClientManager") -> None:
+        """
+        Set the MCP manager (for lazy initialization).
+
+        Args:
+            mcp_manager: MCP client manager instance
+        """
+        self._mcp = mcp_manager

backend/app/services/context/cache/__init__.py

@@ -0,0 +1,11 @@
+"""
+Context Cache Module.
+
+Provides Redis-based caching for assembled contexts.
+"""
+
+from .context_cache import ContextCache
+
+__all__ = [
+    "ContextCache",
+]

backend/app/services/context/cache/context_cache.py

@@ -0,0 +1,434 @@
+"""
+Context Cache Implementation.
+
+Provides Redis-based caching for context operations including
+assembled contexts, token counts, and scoring results.
+"""
+
+import hashlib
+import json
+import logging
+from typing import TYPE_CHECKING, Any
+
+from ..config import ContextSettings, get_context_settings
+from ..exceptions import CacheError
+from ..types import AssembledContext, BaseContext
+
+if TYPE_CHECKING:
+    from redis.asyncio import Redis
+
+logger = logging.getLogger(__name__)
+
+
+class ContextCache:
+    """
+    Redis-based caching for context operations.
+
+    Provides caching for:
+    - Assembled contexts (fingerprint-based)
+    - Token counts (content hash-based)
+    - Scoring results (context + query hash-based)
+
+    Cache keys use a hierarchical structure:
+    - ctx:assembled:{fingerprint}
+    - ctx:tokens:{model}:{content_hash}
+    - ctx:score:{scorer}:{context_hash}:{query_hash}
+    """
+
+    def __init__(
+        self,
+        redis: "Redis | None" = None,
+        settings: ContextSettings | None = None,
+    ) -> None:
+        """
+        Initialize the context cache.
+
+        Args:
+            redis: Redis connection (optional for testing)
+            settings: Cache settings
+        """
+        self._redis = redis
+        self._settings = settings or get_context_settings()
+        self._prefix = self._settings.cache_prefix
+        self._ttl = self._settings.cache_ttl_seconds
+
+        # In-memory fallback cache when Redis unavailable
+        self._memory_cache: dict[str, tuple[str, float]] = {}
+        self._max_memory_items = self._settings.cache_memory_max_items
+
+    def set_redis(self, redis: "Redis") -> None:
+        """Set Redis connection."""
+        self._redis = redis
+
+    @property
+    def is_enabled(self) -> bool:
+        """Check if caching is enabled and available."""
+        return self._settings.cache_enabled and self._redis is not None
+
+    def _cache_key(self, *parts: str) -> str:
+        """
+        Build a cache key from parts.
+
+        Args:
+            *parts: Key components
+
+        Returns:
+            Colon-separated cache key
+        """
+        return f"{self._prefix}:{':'.join(parts)}"
+
+    @staticmethod
+    def _hash_content(content: str) -> str:
+        """
+        Compute hash of content for cache key.
+
+        Args:
+            content: Content to hash
+
+        Returns:
+            32-character hex hash
+        """
+        return hashlib.sha256(content.encode()).hexdigest()[:32]
+
+    def compute_fingerprint(
+        self,
+        contexts: list[BaseContext],
+        query: str,
+        model: str,
+        project_id: str | None = None,
+        agent_id: str | None = None,
+    ) -> str:
+        """
+        Compute a fingerprint for a context assembly request.
+
+        The fingerprint is based on:
+        - Project and agent IDs (for tenant isolation)
+        - Context content hash and metadata (not full content for performance)
+        - Query string
+        - Target model
+
+        SECURITY: project_id and agent_id MUST be included to prevent
+        cross-tenant cache pollution. Without these, one tenant could
+        receive cached contexts from another tenant with the same query.
+
+        Args:
+            contexts: List of contexts
+            query: Query string
+            model: Model name
+            project_id: Project ID for tenant isolation
+            agent_id: Agent ID for tenant isolation
+
+        Returns:
+            32-character hex fingerprint
+        """
+        # Build a deterministic representation using content hashes for performance
+        # This avoids JSON serializing potentially large content strings
+        context_data = []
+        for ctx in contexts:
+            context_data.append(
+                {
+                    "type": ctx.get_type().value,
+                    "content_hash": self._hash_content(
+                        ctx.content
+                    ),  # Hash instead of full content
+                    "source": ctx.source,
+                    "priority": ctx.priority,  # Already an int
+                }
+            )
+
+        data = {
+            # CRITICAL: Include tenant identifiers for cache isolation
+            "project_id": project_id or "",
+            "agent_id": agent_id or "",
+            "contexts": context_data,
+            "query": query,
+            "model": model,
+        }
+
+        content = json.dumps(data, sort_keys=True)
+        return self._hash_content(content)
+
+    async def get_assembled(
+        self,
+        fingerprint: str,
+    ) -> AssembledContext | None:
+        """
+        Get cached assembled context by fingerprint.
+
+        Args:
+            fingerprint: Assembly fingerprint
+
+        Returns:
+            Cached AssembledContext or None if not found
+        """
+        if not self.is_enabled:
+            return None
+
+        key = self._cache_key("assembled", fingerprint)
+
+        try:
+            data = await self._redis.get(key)  # type: ignore
+            if data:
+                logger.debug(f"Cache hit for assembled context: {fingerprint}")
+                result = AssembledContext.from_json(data)
+                result.cache_hit = True
+                result.cache_key = fingerprint
+                return result
+        except Exception as e:
+            logger.warning(f"Cache get error: {e}")
+            raise CacheError(f"Failed to get assembled context: {e}") from e
+
+        return None
+
+    async def set_assembled(
+        self,
+        fingerprint: str,
+        context: AssembledContext,
+        ttl: int | None = None,
+    ) -> None:
+        """
+        Cache an assembled context.
+
+        Args:
+            fingerprint: Assembly fingerprint
+            context: Assembled context to cache
+            ttl: Optional TTL override in seconds
+        """
+        if not self.is_enabled:
+            return
+
+        key = self._cache_key("assembled", fingerprint)
+        expire = ttl or self._ttl
+
+        try:
+            await self._redis.setex(key, expire, context.to_json())  # type: ignore
+            logger.debug(f"Cached assembled context: {fingerprint}")
+        except Exception as e:
+            logger.warning(f"Cache set error: {e}")
+            raise CacheError(f"Failed to cache assembled context: {e}") from e
+
+    async def get_token_count(
+        self,
+        content: str,
+        model: str | None = None,
+    ) -> int | None:
+        """
+        Get cached token count.
+
+        Args:
+            content: Content to look up
+            model: Model name for model-specific tokenization
+
+        Returns:
+            Cached token count or None if not found
+        """
+        model_key = model or "default"
+        content_hash = self._hash_content(content)
+        key = self._cache_key("tokens", model_key, content_hash)
+
+        # Try in-memory first
+        if key in self._memory_cache:
+            return int(self._memory_cache[key][0])
+
+        if not self.is_enabled:
+            return None
+
+        try:
+            data = await self._redis.get(key)  # type: ignore
+            if data:
+                count = int(data)
+                # Store in memory for faster subsequent access
+                self._set_memory(key, str(count))
+                return count
+        except Exception as e:
+            logger.warning(f"Cache get error for tokens: {e}")
+
+        return None
+
+    async def set_token_count(
+        self,
+        content: str,
+        count: int,
+        model: str | None = None,
+        ttl: int | None = None,
+    ) -> None:
+        """
+        Cache a token count.
+
+        Args:
+            content: Content that was counted
+            count: Token count
+            model: Model name
+            ttl: Optional TTL override in seconds
+        """
+        model_key = model or "default"
+        content_hash = self._hash_content(content)
+        key = self._cache_key("tokens", model_key, content_hash)
+        expire = ttl or self._ttl
+
+        # Always store in memory
+        self._set_memory(key, str(count))
+
+        if not self.is_enabled:
+            return
+
+        try:
+            await self._redis.setex(key, expire, str(count))  # type: ignore
+        except Exception as e:
+            logger.warning(f"Cache set error for tokens: {e}")
+
+    async def get_score(
+        self,
+        scorer_name: str,
+        context_id: str,
+        query: str,
+    ) -> float | None:
+        """
+        Get cached score.
+
+        Args:
+            scorer_name: Name of the scorer
+            context_id: Context identifier
+            query: Query string
+
+        Returns:
+            Cached score or None if not found
+        """
+        query_hash = self._hash_content(query)[:16]
+        key = self._cache_key("score", scorer_name, context_id, query_hash)
+
+        # Try in-memory first
+        if key in self._memory_cache:
+            return float(self._memory_cache[key][0])
+
+        if not self.is_enabled:
+            return None
+
+        try:
+            data = await self._redis.get(key)  # type: ignore
+            if data:
+                score = float(data)
+                self._set_memory(key, str(score))
+                return score
+        except Exception as e:
+            logger.warning(f"Cache get error for score: {e}")
+
+        return None
+
+    async def set_score(
+        self,
+        scorer_name: str,
+        context_id: str,
+        query: str,
+        score: float,
+        ttl: int | None = None,
+    ) -> None:
+        """
+        Cache a score.
+
+        Args:
+            scorer_name: Name of the scorer
+            context_id: Context identifier
+            query: Query string
+            score: Score value
+            ttl: Optional TTL override in seconds
+        """
+        query_hash = self._hash_content(query)[:16]
+        key = self._cache_key("score", scorer_name, context_id, query_hash)
+        expire = ttl or self._ttl
+
+        # Always store in memory
+        self._set_memory(key, str(score))
+
+        if not self.is_enabled:
+            return
+
+        try:
+            await self._redis.setex(key, expire, str(score))  # type: ignore
+        except Exception as e:
+            logger.warning(f"Cache set error for score: {e}")
+
+    async def invalidate(self, pattern: str) -> int:
+        """
+        Invalidate cache entries matching a pattern.
+
+        Args:
+            pattern: Key pattern (supports * wildcard)
+
+        Returns:
+            Number of keys deleted
+        """
+        if not self.is_enabled:
+            return 0
+
+        full_pattern = self._cache_key(pattern)
+        deleted = 0
+
+        try:
+            async for key in self._redis.scan_iter(match=full_pattern):  # type: ignore
+                await self._redis.delete(key)  # type: ignore
+                deleted += 1
+
+            logger.info(f"Invalidated {deleted} cache entries matching {pattern}")
+        except Exception as e:
+            logger.warning(f"Cache invalidation error: {e}")
+            raise CacheError(f"Failed to invalidate cache: {e}") from e
+
+        return deleted
+
+    async def clear_all(self) -> int:
+        """
+        Clear all context cache entries.
+
+        Returns:
+            Number of keys deleted
+        """
+        self._memory_cache.clear()
+        return await self.invalidate("*")
+
+    def _set_memory(self, key: str, value: str) -> None:
+        """
+        Set a value in the memory cache.
+
+        Uses LRU-style eviction when max items reached.
+
+        Args:
+            key: Cache key
+            value: Value to store
+        """
+        import time
+
+        if len(self._memory_cache) >= self._max_memory_items:
+            # Evict oldest entries
+            sorted_keys = sorted(
+                self._memory_cache.keys(),
+                key=lambda k: self._memory_cache[k][1],
+            )
+            for k in sorted_keys[: len(sorted_keys) // 2]:
+                del self._memory_cache[k]
+
+        self._memory_cache[key] = (value, time.time())
+
+    async def get_stats(self) -> dict[str, Any]:
+        """
+        Get cache statistics.
+
+        Returns:
+            Dictionary with cache stats
+        """
+        stats = {
+            "enabled": self._settings.cache_enabled,
+            "redis_available": self._redis is not None,
+            "memory_items": len(self._memory_cache),
+            "ttl_seconds": self._ttl,
+        }
+
+        if self.is_enabled:
+            try:
+                # Get Redis info
+                info = await self._redis.info("memory")  # type: ignore
+                stats["redis_memory_used"] = info.get("used_memory_human", "unknown")
+            except Exception as e:
+                logger.debug(f"Failed to get Redis stats: {e}")
+
+        return stats

backend/app/services/context/compression/__init__.py

@@ -0,0 +1,13 @@
+"""
+Context Compression Module.
+
+Provides truncation and compression strategies.
+"""
+
+from .truncation import ContextCompressor, TruncationResult, TruncationStrategy
+
+__all__ = [
+    "ContextCompressor",
+    "TruncationResult",
+    "TruncationStrategy",
+]

backend/app/services/context/compression/truncation.py

@@ -0,0 +1,453 @@
+"""
+Smart Truncation for Context Compression.
+
+Provides intelligent truncation strategies to reduce context size
+while preserving the most important information.
+"""
+
+import logging
+import re
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+from ..config import ContextSettings, get_context_settings
+from ..types import BaseContext, ContextType
+
+if TYPE_CHECKING:
+    from ..budget import TokenBudget, TokenCalculator
+
+logger = logging.getLogger(__name__)
+
+
+def _estimate_tokens(text: str, model: str | None = None) -> int:
+    """
+    Estimate token count using model-specific character ratios.
+
+    Module-level function for reuse across classes. Uses the same ratios
+    as TokenCalculator for consistency.
+
+    Args:
+        text: Text to estimate tokens for
+        model: Optional model name for model-specific ratios
+
+    Returns:
+        Estimated token count (minimum 1)
+    """
+    # Model-specific character ratios (chars per token)
+    model_ratios = {
+        "claude": 3.5,
+        "gpt-4": 4.0,
+        "gpt-3.5": 4.0,
+        "gemini": 4.0,
+    }
+    default_ratio = 4.0
+
+    ratio = default_ratio
+    if model:
+        model_lower = model.lower()
+        for model_prefix, model_ratio in model_ratios.items():
+            if model_prefix in model_lower:
+                ratio = model_ratio
+                break
+
+    return max(1, int(len(text) / ratio))
+
+
+@dataclass
+class TruncationResult:
+    """Result of truncation operation."""
+
+    original_tokens: int
+    truncated_tokens: int
+    content: str
+    truncated: bool
+    truncation_ratio: float  # 0.0 = no truncation, 1.0 = completely removed
+
+    @property
+    def tokens_saved(self) -> int:
+        """Calculate tokens saved by truncation."""
+        return self.original_tokens - self.truncated_tokens
+
+
+class TruncationStrategy:
+    """
+    Smart truncation strategies for context compression.
+
+    Strategies:
+    1. End truncation: Cut from end (for knowledge/docs)
+    2. Middle truncation: Keep start and end (for code)
+    3. Sentence-aware: Truncate at sentence boundaries
+    4. Semantic chunking: Keep most relevant chunks
+    """
+
+    def __init__(
+        self,
+        calculator: "TokenCalculator | None" = None,
+        preserve_ratio_start: float | None = None,
+        min_content_length: int | None = None,
+        settings: ContextSettings | None = None,
+    ) -> None:
+        """
+        Initialize truncation strategy.
+
+        Args:
+            calculator: Token calculator for accurate counting
+            preserve_ratio_start: Ratio of content to keep from start (overrides settings)
+            min_content_length: Minimum characters to preserve (overrides settings)
+            settings: Context settings (uses global if None)
+        """
+        self._settings = settings or get_context_settings()
+        self._calculator = calculator
+
+        # Use provided values or fall back to settings
+        self._preserve_ratio_start = (
+            preserve_ratio_start
+            if preserve_ratio_start is not None
+            else self._settings.truncation_preserve_ratio
+        )
+        self._min_content_length = (
+            min_content_length
+            if min_content_length is not None
+            else self._settings.truncation_min_content_length
+        )
+
+    @property
+    def truncation_marker(self) -> str:
+        """Get truncation marker from settings."""
+        return self._settings.truncation_marker
+
+    def set_calculator(self, calculator: "TokenCalculator") -> None:
+        """Set token calculator."""
+        self._calculator = calculator
+
+    async def truncate_to_tokens(
+        self,
+        content: str,
+        max_tokens: int,
+        strategy: str = "end",
+        model: str | None = None,
+    ) -> TruncationResult:
+        """
+        Truncate content to fit within token limit.
+
+        Args:
+            content: Content to truncate
+            max_tokens: Maximum tokens allowed
+            strategy: Truncation strategy ('end', 'middle', 'sentence')
+            model: Model for token counting
+
+        Returns:
+            TruncationResult with truncated content
+        """
+        if not content:
+            return TruncationResult(
+                original_tokens=0,
+                truncated_tokens=0,
+                content="",
+                truncated=False,
+                truncation_ratio=0.0,
+            )
+
+        # Get original token count
+        original_tokens = await self._count_tokens(content, model)
+
+        if original_tokens <= max_tokens:
+            return TruncationResult(
+                original_tokens=original_tokens,
+                truncated_tokens=original_tokens,
+                content=content,
+                truncated=False,
+                truncation_ratio=0.0,
+            )
+
+        # Apply truncation strategy
+        if strategy == "middle":
+            truncated = await self._truncate_middle(content, max_tokens, model)
+        elif strategy == "sentence":
+            truncated = await self._truncate_sentence(content, max_tokens, model)
+        else:  # "end"
+            truncated = await self._truncate_end(content, max_tokens, model)
+
+        truncated_tokens = await self._count_tokens(truncated, model)
+
+        return TruncationResult(
+            original_tokens=original_tokens,
+            truncated_tokens=truncated_tokens,
+            content=truncated,
+            truncated=True,
+            truncation_ratio=0.0
+            if original_tokens == 0
+            else 1 - (truncated_tokens / original_tokens),
+        )
+
+    async def _truncate_end(
+        self,
+        content: str,
+        max_tokens: int,
+        model: str | None = None,
+    ) -> str:
+        """
+        Truncate from end of content.
+
+        Simple but effective for most content types.
+        """
+        # Binary search for optimal truncation point
+        marker_tokens = await self._count_tokens(self.truncation_marker, model)
+        available_tokens = max(0, max_tokens - marker_tokens)
+
+        # Edge case: if no tokens available for content, return just the marker
+        if available_tokens <= 0:
+            return self.truncation_marker
+
+        # Estimate characters per token (guard against division by zero)
+        content_tokens = await self._count_tokens(content, model)
+        if content_tokens == 0:
+            return content + self.truncation_marker
+        chars_per_token = len(content) / content_tokens
+
+        # Start with estimated position
+        estimated_chars = int(available_tokens * chars_per_token)
+        truncated = content[:estimated_chars]
+
+        # Refine with binary search
+        low, high = len(truncated) // 2, len(truncated)
+        best = truncated
+
+        for _ in range(5):  # Max 5 iterations
+            mid = (low + high) // 2
+            candidate = content[:mid]
+            tokens = await self._count_tokens(candidate, model)
+
+            if tokens <= available_tokens:
+                best = candidate
+                low = mid + 1
+            else:
+                high = mid - 1
+
+        return best + self.truncation_marker
+
+    async def _truncate_middle(
+        self,
+        content: str,
+        max_tokens: int,
+        model: str | None = None,
+    ) -> str:
+        """
+        Truncate from middle, keeping start and end.
+
+        Good for code or content where context at boundaries matters.
+        """
+        marker_tokens = await self._count_tokens(self.truncation_marker, model)
+        available_tokens = max_tokens - marker_tokens
+
+        # Split between start and end
+        start_tokens = int(available_tokens * self._preserve_ratio_start)
+        end_tokens = available_tokens - start_tokens
+
+        # Get start portion
+        start_content = await self._get_content_for_tokens(
+            content, start_tokens, from_start=True, model=model
+        )
+
+        # Get end portion
+        end_content = await self._get_content_for_tokens(
+            content, end_tokens, from_start=False, model=model
+        )
+
+        return start_content + self.truncation_marker + end_content
+
+    async def _truncate_sentence(
+        self,
+        content: str,
+        max_tokens: int,
+        model: str | None = None,
+    ) -> str:
+        """
+        Truncate at sentence boundaries.
+
+        Produces cleaner output by not cutting mid-sentence.
+        """
+        # Split into sentences
+        sentences = re.split(r"(?<=[.!?])\s+", content)
+
+        result: list[str] = []
+        total_tokens = 0
+        marker_tokens = await self._count_tokens(self.truncation_marker, model)
+        available = max_tokens - marker_tokens
+
+        for sentence in sentences:
+            sentence_tokens = await self._count_tokens(sentence, model)
+            if total_tokens + sentence_tokens <= available:
+                result.append(sentence)
+                total_tokens += sentence_tokens
+            else:
+                break
+
+        if len(result) < len(sentences):
+            return " ".join(result) + self.truncation_marker
+        return " ".join(result)
+
+    async def _get_content_for_tokens(
+        self,
+        content: str,
+        target_tokens: int,
+        from_start: bool = True,
+        model: str | None = None,
+    ) -> str:
+        """Get portion of content fitting within token limit."""
+        if target_tokens <= 0:
+            return ""
+
+        current_tokens = await self._count_tokens(content, model)
+        if current_tokens <= target_tokens:
+            return content
+
+        # Estimate characters (guard against division by zero)
+        if current_tokens == 0:
+            return content
+        chars_per_token = len(content) / current_tokens
+        estimated_chars = int(target_tokens * chars_per_token)
+
+        if from_start:
+            return content[:estimated_chars]
+        else:
+            return content[-estimated_chars:]
+
+    async def _count_tokens(self, text: str, model: str | None = None) -> int:
+        """Count tokens using calculator or estimation."""
+        if self._calculator is not None:
+            return await self._calculator.count_tokens(text, model)
+
+        # Fallback estimation with model-specific ratios
+        return _estimate_tokens(text, model)
+
+
+class ContextCompressor:
+    """
+    Compresses contexts to fit within budget constraints.
+
+    Uses truncation strategies to reduce context size while
+    preserving the most important information.
+    """
+
+    def __init__(
+        self,
+        truncation: TruncationStrategy | None = None,
+        calculator: "TokenCalculator | None" = None,
+    ) -> None:
+        """
+        Initialize context compressor.
+
+        Args:
+            truncation: Truncation strategy to use
+            calculator: Token calculator for counting
+        """
+        self._truncation = truncation or TruncationStrategy(calculator)
+        self._calculator = calculator
+
+        if calculator:
+            self._truncation.set_calculator(calculator)
+
+    def set_calculator(self, calculator: "TokenCalculator") -> None:
+        """Set token calculator."""
+        self._calculator = calculator
+        self._truncation.set_calculator(calculator)
+
+    async def compress_context(
+        self,
+        context: BaseContext,
+        max_tokens: int,
+        model: str | None = None,
+    ) -> BaseContext:
+        """
+        Compress a single context to fit token limit.
+
+        Args:
+            context: Context to compress
+            max_tokens: Maximum tokens allowed
+            model: Model for token counting
+
+        Returns:
+            Compressed context (may be same object if no compression needed)
+        """
+        current_tokens = context.token_count or await self._count_tokens(
+            context.content, model
+        )
+
+        if current_tokens <= max_tokens:
+            return context
+
+        # Choose strategy based on context type
+        strategy = self._get_strategy_for_type(context.get_type())
+
+        result = await self._truncation.truncate_to_tokens(
+            content=context.content,
+            max_tokens=max_tokens,
+            strategy=strategy,
+            model=model,
+        )
+
+        # Update context with truncated content
+        context.content = result.content
+        context.token_count = result.truncated_tokens
+        context.metadata["truncated"] = True
+        context.metadata["original_tokens"] = result.original_tokens
+
+        return context
+
+    async def compress_contexts(
+        self,
+        contexts: list[BaseContext],
+        budget: "TokenBudget",
+        model: str | None = None,
+    ) -> list[BaseContext]:
+        """
+        Compress multiple contexts to fit within budget.
+
+        Args:
+            contexts: Contexts to potentially compress
+            budget: Token budget constraints
+            model: Model for token counting
+
+        Returns:
+            List of contexts (compressed as needed)
+        """
+        result: list[BaseContext] = []
+
+        for context in contexts:
+            context_type = context.get_type()
+            remaining = budget.remaining(context_type)
+            current_tokens = context.token_count or await self._count_tokens(
+                context.content, model
+            )
+
+            if current_tokens > remaining:
+                # Need to compress
+                compressed = await self.compress_context(context, remaining, model)
+                result.append(compressed)
+                logger.debug(
+                    f"Compressed {context_type.value} context from "
+                    f"{current_tokens} to {compressed.token_count} tokens"
+                )
+            else:
+                result.append(context)
+
+        return result
+
+    def _get_strategy_for_type(self, context_type: ContextType) -> str:
+        """Get optimal truncation strategy for context type."""
+        strategies = {
+            ContextType.SYSTEM: "end",  # Keep instructions at start
+            ContextType.TASK: "end",  # Keep task description start
+            ContextType.KNOWLEDGE: "sentence",  # Clean sentence boundaries
+            ContextType.CONVERSATION: "end",  # Keep recent conversation
+            ContextType.TOOL: "middle",  # Keep command and result summary
+        }
+        return strategies.get(context_type, "end")
+
+    async def _count_tokens(self, text: str, model: str | None = None) -> int:
+        """Count tokens using calculator or estimation."""
+        if self._calculator is not None:
+            return await self._calculator.count_tokens(text, model)
+        # Use model-specific estimation for consistency
+        return _estimate_tokens(text, model)

backend/app/services/context/config.py

@@ -0,0 +1,380 @@
+"""
+Context Management Engine Configuration.
+
+Provides Pydantic settings for context assembly,
+token budget allocation, and caching.
+"""
+
+import threading
+from functools import lru_cache
+from typing import Any
+
+from pydantic import Field, field_validator, model_validator
+from pydantic_settings import BaseSettings
+
+
+class ContextSettings(BaseSettings):
+    """
+    Configuration for the Context Management Engine.
+
+    All settings can be overridden via environment variables
+    with the CTX_ prefix.
+    """
+
+    # Budget allocation percentages (must sum to 1.0)
+    budget_system: float = Field(
+        default=0.05,
+        ge=0.0,
+        le=1.0,
+        description="Percentage of budget for system prompts (5%)",
+    )
+    budget_task: float = Field(
+        default=0.10,
+        ge=0.0,
+        le=1.0,
+        description="Percentage of budget for task context (10%)",
+    )
+    budget_knowledge: float = Field(
+        default=0.40,
+        ge=0.0,
+        le=1.0,
+        description="Percentage of budget for RAG/knowledge (40%)",
+    )
+    budget_conversation: float = Field(
+        default=0.20,
+        ge=0.0,
+        le=1.0,
+        description="Percentage of budget for conversation history (20%)",
+    )
+    budget_tools: float = Field(
+        default=0.05,
+        ge=0.0,
+        le=1.0,
+        description="Percentage of budget for tool descriptions (5%)",
+    )
+    budget_response: float = Field(
+        default=0.15,
+        ge=0.0,
+        le=1.0,
+        description="Percentage reserved for response (15%)",
+    )
+    budget_buffer: float = Field(
+        default=0.05,
+        ge=0.0,
+        le=1.0,
+        description="Percentage buffer for safety margin (5%)",
+    )
+
+    # Scoring weights
+    scoring_relevance_weight: float = Field(
+        default=0.5,
+        ge=0.0,
+        le=1.0,
+        description="Weight for relevance scoring",
+    )
+    scoring_recency_weight: float = Field(
+        default=0.3,
+        ge=0.0,
+        le=1.0,
+        description="Weight for recency scoring",
+    )
+    scoring_priority_weight: float = Field(
+        default=0.2,
+        ge=0.0,
+        le=1.0,
+        description="Weight for priority scoring",
+    )
+
+    # Recency decay settings
+    recency_decay_hours: float = Field(
+        default=24.0,
+        gt=0.0,
+        description="Hours until recency score decays to 50%",
+    )
+    recency_max_age_hours: float = Field(
+        default=168.0,
+        gt=0.0,
+        description="Hours until context is considered stale (7 days)",
+    )
+
+    # Compression settings
+    compression_threshold: float = Field(
+        default=0.8,
+        ge=0.0,
+        le=1.0,
+        description="Compress when budget usage exceeds this percentage",
+    )
+    truncation_marker: str = Field(
+        default="\n\n[...content truncated...]\n\n",
+        description="Marker text to insert where content was truncated",
+    )
+    truncation_preserve_ratio: float = Field(
+        default=0.7,
+        ge=0.1,
+        le=0.9,
+        description="Ratio of content to preserve from start in middle truncation (0.7 = 70% start, 30% end)",
+    )
+    truncation_min_content_length: int = Field(
+        default=100,
+        ge=10,
+        le=1000,
+        description="Minimum content length in characters before truncation applies",
+    )
+    summary_model_group: str = Field(
+        default="fast",
+        description="Model group to use for summarization",
+    )
+
+    # Caching settings
+    cache_enabled: bool = Field(
+        default=True,
+        description="Enable Redis caching for assembled contexts",
+    )
+    cache_ttl_seconds: int = Field(
+        default=3600,
+        ge=60,
+        le=86400,
+        description="Cache TTL in seconds (1 hour default, max 24 hours)",
+    )
+    cache_prefix: str = Field(
+        default="ctx",
+        description="Redis key prefix for context cache",
+    )
+    cache_memory_max_items: int = Field(
+        default=1000,
+        ge=100,
+        le=100000,
+        description="Maximum items in memory fallback cache when Redis unavailable",
+    )
+
+    # Performance settings
+    max_assembly_time_ms: int = Field(
+        default=2000,
+        ge=10,
+        le=30000,
+        description="Maximum time for context assembly in milliseconds. "
+        "Should be high enough to accommodate MCP calls for knowledge retrieval.",
+    )
+    parallel_scoring: bool = Field(
+        default=True,
+        description="Score contexts in parallel for better performance",
+    )
+    max_parallel_scores: int = Field(
+        default=10,
+        ge=1,
+        le=50,
+        description="Maximum number of contexts to score in parallel",
+    )
+
+    # Knowledge retrieval settings
+    knowledge_search_type: str = Field(
+        default="hybrid",
+        description="Default search type for knowledge retrieval",
+    )
+    knowledge_max_results: int = Field(
+        default=10,
+        ge=1,
+        le=50,
+        description="Maximum knowledge chunks to retrieve",
+    )
+    knowledge_min_score: float = Field(
+        default=0.5,
+        ge=0.0,
+        le=1.0,
+        description="Minimum relevance score for knowledge",
+    )
+
+    # Relevance scoring settings
+    relevance_keyword_fallback_weight: float = Field(
+        default=0.5,
+        ge=0.0,
+        le=1.0,
+        description="Maximum score for keyword-based fallback scoring (when semantic unavailable)",
+    )
+    relevance_semantic_max_chars: int = Field(
+        default=2000,
+        ge=100,
+        le=10000,
+        description="Maximum content length in chars for semantic similarity computation",
+    )
+
+    # Diversity/ranking settings
+    diversity_max_per_source: int = Field(
+        default=3,
+        ge=1,
+        le=20,
+        description="Maximum contexts from the same source in diversity reranking",
+    )
+
+    # Conversation history settings
+    conversation_max_turns: int = Field(
+        default=20,
+        ge=1,
+        le=100,
+        description="Maximum conversation turns to include",
+    )
+    conversation_recent_priority: bool = Field(
+        default=True,
+        description="Prioritize recent conversation turns",
+    )
+
+    @field_validator("knowledge_search_type")
+    @classmethod
+    def validate_search_type(cls, v: str) -> str:
+        """Validate search type is valid."""
+        valid_types = {"semantic", "keyword", "hybrid"}
+        if v not in valid_types:
+            raise ValueError(f"search_type must be one of: {valid_types}")
+        return v
+
+    @model_validator(mode="after")
+    def validate_budget_allocation(self) -> "ContextSettings":
+        """Validate that budget percentages sum to 1.0."""
+        total = (
+            self.budget_system
+            + self.budget_task
+            + self.budget_knowledge
+            + self.budget_conversation
+            + self.budget_tools
+            + self.budget_response
+            + self.budget_buffer
+        )
+        # Allow small floating point error
+        if abs(total - 1.0) > 0.001:
+            raise ValueError(
+                f"Budget percentages must sum to 1.0, got {total:.3f}. "
+                f"Current allocation: system={self.budget_system}, task={self.budget_task}, "
+                f"knowledge={self.budget_knowledge}, conversation={self.budget_conversation}, "
+                f"tools={self.budget_tools}, response={self.budget_response}, buffer={self.budget_buffer}"
+            )
+        return self
+
+    @model_validator(mode="after")
+    def validate_scoring_weights(self) -> "ContextSettings":
+        """Validate that scoring weights sum to 1.0."""
+        total = (
+            self.scoring_relevance_weight
+            + self.scoring_recency_weight
+            + self.scoring_priority_weight
+        )
+        # Allow small floating point error
+        if abs(total - 1.0) > 0.001:
+            raise ValueError(
+                f"Scoring weights must sum to 1.0, got {total:.3f}. "
+                f"Current weights: relevance={self.scoring_relevance_weight}, "
+                f"recency={self.scoring_recency_weight}, priority={self.scoring_priority_weight}"
+            )
+        return self
+
+    def get_budget_allocation(self) -> dict[str, float]:
+        """Get budget allocation as a dictionary."""
+        return {
+            "system": self.budget_system,
+            "task": self.budget_task,
+            "knowledge": self.budget_knowledge,
+            "conversation": self.budget_conversation,
+            "tools": self.budget_tools,
+            "response": self.budget_response,
+            "buffer": self.budget_buffer,
+        }
+
+    def get_scoring_weights(self) -> dict[str, float]:
+        """Get scoring weights as a dictionary."""
+        return {
+            "relevance": self.scoring_relevance_weight,
+            "recency": self.scoring_recency_weight,
+            "priority": self.scoring_priority_weight,
+        }
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert settings to dictionary for logging/debugging."""
+        return {
+            "budget": self.get_budget_allocation(),
+            "scoring": self.get_scoring_weights(),
+            "compression": {
+                "threshold": self.compression_threshold,
+                "summary_model_group": self.summary_model_group,
+                "truncation_marker": self.truncation_marker,
+                "truncation_preserve_ratio": self.truncation_preserve_ratio,
+                "truncation_min_content_length": self.truncation_min_content_length,
+            },
+            "cache": {
+                "enabled": self.cache_enabled,
+                "ttl_seconds": self.cache_ttl_seconds,
+                "prefix": self.cache_prefix,
+                "memory_max_items": self.cache_memory_max_items,
+            },
+            "performance": {
+                "max_assembly_time_ms": self.max_assembly_time_ms,
+                "parallel_scoring": self.parallel_scoring,
+                "max_parallel_scores": self.max_parallel_scores,
+            },
+            "knowledge": {
+                "search_type": self.knowledge_search_type,
+                "max_results": self.knowledge_max_results,
+                "min_score": self.knowledge_min_score,
+            },
+            "relevance": {
+                "keyword_fallback_weight": self.relevance_keyword_fallback_weight,
+                "semantic_max_chars": self.relevance_semantic_max_chars,
+            },
+            "diversity": {
+                "max_per_source": self.diversity_max_per_source,
+            },
+            "conversation": {
+                "max_turns": self.conversation_max_turns,
+                "recent_priority": self.conversation_recent_priority,
+            },
+        }
+
+    model_config = {
+        "env_prefix": "CTX_",
+        "env_file": "../.env",
+        "env_file_encoding": "utf-8",
+        "case_sensitive": False,
+        "extra": "ignore",
+    }
+
+
+# Thread-safe singleton pattern
+_settings: ContextSettings | None = None
+_settings_lock = threading.Lock()
+
+
+def get_context_settings() -> ContextSettings:
+    """
+    Get the global ContextSettings instance.
+
+    Thread-safe with double-checked locking pattern.
+
+    Returns:
+        ContextSettings instance
+    """
+    global _settings
+    if _settings is None:
+        with _settings_lock:
+            if _settings is None:
+                _settings = ContextSettings()
+    return _settings
+
+
+def reset_context_settings() -> None:
+    """
+    Reset the global settings instance.
+
+    Primarily used for testing.
+    """
+    global _settings
+    with _settings_lock:
+        _settings = None
+
+
+@lru_cache(maxsize=1)
+def get_default_settings() -> ContextSettings:
+    """
+    Get default settings (cached).
+
+    Use this for read-only access to defaults.
+    For mutable access, use get_context_settings().
+    """
+    return ContextSettings()

backend/app/services/context/engine.py

@@ -0,0 +1,582 @@
+"""
+Context Management Engine.
+
+Main orchestration layer for context assembly and optimization.
+Provides a high-level API for assembling optimized context for LLM requests.
+"""
+
+import logging
+from typing import TYPE_CHECKING, Any
+from uuid import UUID
+
+from .assembly import ContextPipeline
+from .budget import BudgetAllocator, TokenBudget, TokenCalculator
+from .cache import ContextCache
+from .compression import ContextCompressor
+from .config import ContextSettings, get_context_settings
+from .prioritization import ContextRanker
+from .scoring import CompositeScorer
+from .types import (
+    AssembledContext,
+    BaseContext,
+    ConversationContext,
+    KnowledgeContext,
+    MemoryContext,
+    MessageRole,
+    SystemContext,
+    TaskContext,
+    ToolContext,
+)
+
+if TYPE_CHECKING:
+    from redis.asyncio import Redis
+
+    from app.services.mcp.client_manager import MCPClientManager
+    from app.services.memory.integration import MemoryContextSource
+
+logger = logging.getLogger(__name__)
+
+
+class ContextEngine:
+    """
+    Main context management engine.
+
+    Provides high-level API for context assembly and optimization.
+    Integrates all components: scoring, ranking, compression, formatting, and caching.
+
+    Usage:
+        engine = ContextEngine(mcp_manager=mcp, redis=redis)
+
+        # Assemble context for an LLM request
+        result = await engine.assemble_context(
+            project_id="proj-123",
+            agent_id="agent-456",
+            query="implement user authentication",
+            model="claude-3-sonnet",
+            system_prompt="You are an expert developer.",
+            knowledge_query="authentication best practices",
+        )
+
+        # Use the assembled context
+        print(result.content)
+        print(f"Tokens: {result.total_tokens}")
+    """
+
+    def __init__(
+        self,
+        mcp_manager: "MCPClientManager | None" = None,
+        redis: "Redis | None" = None,
+        settings: ContextSettings | None = None,
+        memory_source: "MemoryContextSource | None" = None,
+    ) -> None:
+        """
+        Initialize the context engine.
+
+        Args:
+            mcp_manager: MCP client manager for LLM Gateway/Knowledge Base
+            redis: Redis connection for caching
+            settings: Context settings
+            memory_source: Optional memory context source for agent memory
+        """
+        self._mcp = mcp_manager
+        self._settings = settings or get_context_settings()
+        self._memory_source = memory_source
+
+        # Initialize components
+        self._calculator = TokenCalculator(mcp_manager=mcp_manager)
+        self._scorer = CompositeScorer(mcp_manager=mcp_manager, settings=self._settings)
+        self._ranker = ContextRanker(scorer=self._scorer, calculator=self._calculator)
+        self._compressor = ContextCompressor(calculator=self._calculator)
+        self._allocator = BudgetAllocator(self._settings)
+        self._cache = ContextCache(redis=redis, settings=self._settings)
+
+        # Pipeline for assembly
+        self._pipeline = ContextPipeline(
+            mcp_manager=mcp_manager,
+            settings=self._settings,
+            calculator=self._calculator,
+            scorer=self._scorer,
+            ranker=self._ranker,
+            compressor=self._compressor,
+        )
+
+    def set_mcp_manager(self, mcp_manager: "MCPClientManager") -> None:
+        """
+        Set MCP manager for all components.
+
+        Args:
+            mcp_manager: MCP client manager
+        """
+        self._mcp = mcp_manager
+        self._calculator.set_mcp_manager(mcp_manager)
+        self._scorer.set_mcp_manager(mcp_manager)
+        self._pipeline.set_mcp_manager(mcp_manager)
+
+    def set_redis(self, redis: "Redis") -> None:
+        """
+        Set Redis connection for caching.
+
+        Args:
+            redis: Redis connection
+        """
+        self._cache.set_redis(redis)
+
+    def set_memory_source(self, memory_source: "MemoryContextSource") -> None:
+        """
+        Set memory context source for agent memory integration.
+
+        Args:
+            memory_source: Memory context source
+        """
+        self._memory_source = memory_source
+
+    async def assemble_context(
+        self,
+        project_id: str,
+        agent_id: str,
+        query: str,
+        model: str,
+        max_tokens: int | None = None,
+        system_prompt: str | None = None,
+        task_description: str | None = None,
+        knowledge_query: str | None = None,
+        knowledge_limit: int = 10,
+        memory_query: str | None = None,
+        memory_limit: int = 20,
+        session_id: str | None = None,
+        agent_type_id: str | None = None,
+        conversation_history: list[dict[str, str]] | None = None,
+        tool_results: list[dict[str, Any]] | None = None,
+        custom_contexts: list[BaseContext] | None = None,
+        custom_budget: TokenBudget | None = None,
+        compress: bool = True,
+        format_output: bool = True,
+        use_cache: bool = True,
+    ) -> AssembledContext:
+        """
+        Assemble optimized context for an LLM request.
+
+        This is the main entry point for context management.
+        It gathers context from various sources, scores and ranks them,
+        compresses if needed, and formats for the target model.
+
+        Args:
+            project_id: Project identifier
+            agent_id: Agent identifier
+            query: User's query or current request
+            model: Target model name
+            max_tokens: Maximum context tokens (uses model default if None)
+            system_prompt: System prompt/instructions
+            task_description: Current task description
+            knowledge_query: Query for knowledge base search
+            knowledge_limit: Max number of knowledge results
+            memory_query: Query for agent memory search
+            memory_limit: Max number of memory results
+            session_id: Session ID for working memory access
+            agent_type_id: Agent type ID for procedural memory
+            conversation_history: List of {"role": str, "content": str}
+            tool_results: List of tool results to include
+            custom_contexts: Additional custom contexts
+            custom_budget: Custom token budget
+            compress: Whether to apply compression
+            format_output: Whether to format for the model
+            use_cache: Whether to use caching
+
+        Returns:
+            AssembledContext with optimized content
+
+        Raises:
+            AssemblyTimeoutError: If assembly exceeds timeout
+            BudgetExceededError: If context exceeds budget
+        """
+        # Gather all contexts
+        contexts: list[BaseContext] = []
+
+        # 1. System context
+        if system_prompt:
+            contexts.append(
+                SystemContext(
+                    content=system_prompt,
+                    source="system_prompt",
+                )
+            )
+
+        # 2. Task context
+        if task_description:
+            contexts.append(
+                TaskContext(
+                    content=task_description,
+                    source=f"task:{project_id}:{agent_id}",
+                )
+            )
+
+        # 3. Knowledge context from Knowledge Base
+        if knowledge_query and self._mcp:
+            knowledge_contexts = await self._fetch_knowledge(
+                project_id=project_id,
+                agent_id=agent_id,
+                query=knowledge_query,
+                limit=knowledge_limit,
+            )
+            contexts.extend(knowledge_contexts)
+
+        # 4. Memory context from Agent Memory System
+        if memory_query and self._memory_source:
+            memory_contexts = await self._fetch_memory(
+                project_id=project_id,
+                agent_id=agent_id,
+                query=memory_query,
+                limit=memory_limit,
+                session_id=session_id,
+                agent_type_id=agent_type_id,
+            )
+            contexts.extend(memory_contexts)
+
+        # 5. Conversation history
+        if conversation_history:
+            contexts.extend(self._convert_conversation(conversation_history))
+
+        # 6. Tool results
+        if tool_results:
+            contexts.extend(self._convert_tool_results(tool_results))
+
+        # 7. Custom contexts
+        if custom_contexts:
+            contexts.extend(custom_contexts)
+
+        # Check cache if enabled
+        fingerprint: str | None = None
+        if use_cache and self._cache.is_enabled:
+            # Include project_id and agent_id for tenant isolation
+            fingerprint = self._cache.compute_fingerprint(
+                contexts, query, model, project_id=project_id, agent_id=agent_id
+            )
+            cached = await self._cache.get_assembled(fingerprint)
+            if cached:
+                logger.debug(f"Cache hit for context assembly: {fingerprint}")
+                return cached
+
+        # Run assembly pipeline
+        result = await self._pipeline.assemble(
+            contexts=contexts,
+            query=query,
+            model=model,
+            max_tokens=max_tokens,
+            custom_budget=custom_budget,
+            compress=compress,
+            format_output=format_output,
+        )
+
+        # Cache result if enabled (reuse fingerprint computed above)
+        if use_cache and self._cache.is_enabled and fingerprint is not None:
+            await self._cache.set_assembled(fingerprint, result)
+
+        return result
+
+    async def _fetch_knowledge(
+        self,
+        project_id: str,
+        agent_id: str,
+        query: str,
+        limit: int = 10,
+    ) -> list[KnowledgeContext]:
+        """
+        Fetch relevant knowledge from Knowledge Base via MCP.
+
+        Args:
+            project_id: Project identifier
+            agent_id: Agent identifier
+            query: Search query
+            limit: Maximum results
+
+        Returns:
+            List of KnowledgeContext instances
+        """
+        if not self._mcp:
+            return []
+
+        try:
+            result = await self._mcp.call_tool(
+                "knowledge-base",
+                "search_knowledge",
+                {
+                    "project_id": project_id,
+                    "agent_id": agent_id,
+                    "query": query,
+                    "search_type": "hybrid",
+                    "limit": limit,
+                },
+            )
+
+            # Check both ToolResult.success AND response success
+            if not result.success:
+                logger.warning(f"Knowledge search failed: {result.error}")
+                return []
+
+            if not isinstance(result.data, dict) or not result.data.get(
+                "success", True
+            ):
+                logger.warning("Knowledge search returned unsuccessful response")
+                return []
+
+            contexts = []
+            results = result.data.get("results", [])
+            for chunk in results:
+                contexts.append(
+                    KnowledgeContext(
+                        content=chunk.get("content", ""),
+                        source=chunk.get("source_path", "unknown"),
+                        relevance_score=chunk.get("score", 0.0),
+                        metadata={
+                            "chunk_id": chunk.get(
+                                "id"
+                            ),  # Server returns 'id' not 'chunk_id'
+                            "document_id": chunk.get("document_id"),
+                        },
+                    )
+                )
+
+            logger.debug(f"Fetched {len(contexts)} knowledge chunks for query: {query}")
+            return contexts
+
+        except Exception as e:
+            logger.warning(f"Failed to fetch knowledge: {e}")
+            return []
+
+    async def _fetch_memory(
+        self,
+        project_id: str,
+        agent_id: str,
+        query: str,
+        limit: int = 20,
+        session_id: str | None = None,
+        agent_type_id: str | None = None,
+    ) -> list[MemoryContext]:
+        """
+        Fetch relevant memories from Agent Memory System.
+
+        Args:
+            project_id: Project identifier
+            agent_id: Agent identifier
+            query: Search query
+            limit: Maximum results
+            session_id: Session ID for working memory
+            agent_type_id: Agent type ID for procedural memory
+
+        Returns:
+            List of MemoryContext instances
+        """
+        if not self._memory_source:
+            return []
+
+        try:
+            # Import here to avoid circular imports
+
+            # Configure fetch limits
+            from app.services.memory.integration.context_source import MemoryFetchConfig
+
+            config = MemoryFetchConfig(
+                working_limit=min(limit // 4, 5),
+                episodic_limit=min(limit // 2, 10),
+                semantic_limit=min(limit // 2, 10),
+                procedural_limit=min(limit // 4, 5),
+                include_working=session_id is not None,
+            )
+
+            result = await self._memory_source.fetch_context(
+                query=query,
+                project_id=UUID(project_id),
+                agent_instance_id=UUID(agent_id) if agent_id else None,
+                agent_type_id=UUID(agent_type_id) if agent_type_id else None,
+                session_id=session_id,
+                config=config,
+            )
+
+            logger.debug(
+                f"Fetched {len(result.contexts)} memory contexts for query: {query}, "
+                f"by_type: {result.by_type}"
+            )
+            return result.contexts[:limit]
+
+        except Exception as e:
+            logger.warning(f"Failed to fetch memory: {e}")
+            return []
+
+    def _convert_conversation(
+        self,
+        history: list[dict[str, str]],
+    ) -> list[ConversationContext]:
+        """
+        Convert conversation history to ConversationContext instances.
+
+        Args:
+            history: List of {"role": str, "content": str}
+
+        Returns:
+            List of ConversationContext instances
+        """
+        contexts = []
+        for i, turn in enumerate(history):
+            role_str = turn.get("role", "user").lower()
+            role = (
+                MessageRole.ASSISTANT if role_str == "assistant" else MessageRole.USER
+            )
+
+            contexts.append(
+                ConversationContext(
+                    content=turn.get("content", ""),
+                    source=f"conversation:{i}",
+                    role=role,
+                    metadata={"role": role_str, "turn": i},
+                )
+            )
+
+        return contexts
+
+    def _convert_tool_results(
+        self,
+        results: list[dict[str, Any]],
+    ) -> list[ToolContext]:
+        """
+        Convert tool results to ToolContext instances.
+
+        Args:
+            results: List of tool result dictionaries
+
+        Returns:
+            List of ToolContext instances
+        """
+        contexts = []
+        for result in results:
+            tool_name = result.get("tool_name", "unknown")
+            content = result.get("content", result.get("result", ""))
+
+            # Handle dict content
+            if isinstance(content, dict):
+                import json
+
+                content = json.dumps(content, indent=2)
+
+            contexts.append(
+                ToolContext(
+                    content=str(content),
+                    source=f"tool:{tool_name}",
+                    metadata={
+                        "tool_name": tool_name,
+                        "status": result.get("status", "success"),
+                    },
+                )
+            )
+
+        return contexts
+
+    async def get_budget_for_model(
+        self,
+        model: str,
+        max_tokens: int | None = None,
+    ) -> TokenBudget:
+        """
+        Get the token budget for a specific model.
+
+        Args:
+            model: Model name
+            max_tokens: Optional max tokens override
+
+        Returns:
+            TokenBudget instance
+        """
+        if max_tokens:
+            return self._allocator.create_budget(max_tokens)
+        return self._allocator.create_budget_for_model(model)
+
+    async def count_tokens(
+        self,
+        content: str,
+        model: str | None = None,
+    ) -> int:
+        """
+        Count tokens in content.
+
+        Args:
+            content: Content to count
+            model: Model for model-specific tokenization
+
+        Returns:
+            Token count
+        """
+        # Check cache first
+        cached = await self._cache.get_token_count(content, model)
+        if cached is not None:
+            return cached
+
+        count = await self._calculator.count_tokens(content, model)
+
+        # Cache the result
+        await self._cache.set_token_count(content, count, model)
+
+        return count
+
+    async def invalidate_cache(
+        self,
+        project_id: str | None = None,
+        pattern: str | None = None,
+    ) -> int:
+        """
+        Invalidate cache entries.
+
+        Args:
+            project_id: Invalidate all cache for a project
+            pattern: Custom pattern to match
+
+        Returns:
+            Number of entries invalidated
+        """
+        if pattern:
+            return await self._cache.invalidate(pattern)
+        elif project_id:
+            return await self._cache.invalidate(f"*{project_id}*")
+        else:
+            return await self._cache.clear_all()
+
+    async def get_stats(self) -> dict[str, Any]:
+        """
+        Get engine statistics.
+
+        Returns:
+            Dictionary with engine stats
+        """
+        return {
+            "cache": await self._cache.get_stats(),
+            "settings": {
+                "compression_threshold": self._settings.compression_threshold,
+                "max_assembly_time_ms": self._settings.max_assembly_time_ms,
+                "cache_enabled": self._settings.cache_enabled,
+            },
+        }
+
+
+# Convenience factory function
+def create_context_engine(
+    mcp_manager: "MCPClientManager | None" = None,
+    redis: "Redis | None" = None,
+    settings: ContextSettings | None = None,
+    memory_source: "MemoryContextSource | None" = None,
+) -> ContextEngine:
+    """
+    Create a context engine instance.
+
+    Args:
+        mcp_manager: MCP client manager
+        redis: Redis connection
+        settings: Context settings
+        memory_source: Optional memory context source
+
+    Returns:
+        Configured ContextEngine instance
+    """
+    return ContextEngine(
+        mcp_manager=mcp_manager,
+        redis=redis,
+        settings=settings,
+        memory_source=memory_source,
+    )

backend/app/services/context/exceptions.py

@@ -0,0 +1,354 @@
+"""
+Context Management Engine Exceptions.
+
+Provides a hierarchy of exceptions for context assembly,
+token budget management, and related operations.
+"""
+
+from typing import Any
+
+
+class ContextError(Exception):
+    """
+    Base exception for all context management errors.
+
+    All context-related exceptions should inherit from this class
+    to allow for catch-all handling when needed.
+    """
+
+    def __init__(self, message: str, details: dict[str, Any] | None = None) -> None:
+        """
+        Initialize context error.
+
+        Args:
+            message: Human-readable error message
+            details: Optional dict with additional error context
+        """
+        self.message = message
+        self.details = details or {}
+        super().__init__(message)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert exception to dictionary for logging/serialization."""
+        return {
+            "error_type": self.__class__.__name__,
+            "message": self.message,
+            "details": self.details,
+        }
+
+
+class BudgetExceededError(ContextError):
+    """
+    Raised when token budget is exceeded.
+
+    This occurs when the assembled context would exceed the
+    allocated token budget for a specific context type or total.
+    """
+
+    def __init__(
+        self,
+        message: str = "Token budget exceeded",
+        allocated: int = 0,
+        requested: int = 0,
+        context_type: str | None = None,
+    ) -> None:
+        """
+        Initialize budget exceeded error.
+
+        Args:
+            message: Error message
+            allocated: Tokens allocated for this context type
+            requested: Tokens requested
+            context_type: Type of context that exceeded budget
+        """
+        details: dict[str, Any] = {
+            "allocated": allocated,
+            "requested": requested,
+            "overage": requested - allocated,
+        }
+        if context_type:
+            details["context_type"] = context_type
+
+        super().__init__(message, details)
+        self.allocated = allocated
+        self.requested = requested
+        self.context_type = context_type
+
+
+class TokenCountError(ContextError):
+    """
+    Raised when token counting fails.
+
+    This typically occurs when the LLM Gateway token counting
+    service is unavailable or returns an error.
+    """
+
+    def __init__(
+        self,
+        message: str = "Failed to count tokens",
+        model: str | None = None,
+        text_length: int | None = None,
+    ) -> None:
+        """
+        Initialize token count error.
+
+        Args:
+            message: Error message
+            model: Model for which counting was attempted
+            text_length: Length of text that failed to count
+        """
+        details: dict[str, Any] = {}
+        if model:
+            details["model"] = model
+        if text_length is not None:
+            details["text_length"] = text_length
+
+        super().__init__(message, details)
+        self.model = model
+        self.text_length = text_length
+
+
+class CompressionError(ContextError):
+    """
+    Raised when context compression fails.
+
+    This can occur when summarization or truncation cannot
+    reduce content to fit within the budget.
+    """
+
+    def __init__(
+        self,
+        message: str = "Failed to compress context",
+        original_tokens: int | None = None,
+        target_tokens: int | None = None,
+        achieved_tokens: int | None = None,
+    ) -> None:
+        """
+        Initialize compression error.
+
+        Args:
+            message: Error message
+            original_tokens: Tokens before compression
+            target_tokens: Target token count
+            achieved_tokens: Tokens achieved after compression attempt
+        """
+        details: dict[str, Any] = {}
+        if original_tokens is not None:
+            details["original_tokens"] = original_tokens
+        if target_tokens is not None:
+            details["target_tokens"] = target_tokens
+        if achieved_tokens is not None:
+            details["achieved_tokens"] = achieved_tokens
+
+        super().__init__(message, details)
+        self.original_tokens = original_tokens
+        self.target_tokens = target_tokens
+        self.achieved_tokens = achieved_tokens
+
+
+class AssemblyTimeoutError(ContextError):
+    """
+    Raised when context assembly exceeds time limit.
+
+    Context assembly must complete within a configurable
+    time limit to maintain responsiveness.
+    """
+
+    def __init__(
+        self,
+        message: str = "Context assembly timed out",
+        timeout_ms: int = 0,
+        elapsed_ms: float = 0.0,
+        stage: str | None = None,
+    ) -> None:
+        """
+        Initialize assembly timeout error.
+
+        Args:
+            message: Error message
+            timeout_ms: Configured timeout in milliseconds
+            elapsed_ms: Actual elapsed time in milliseconds
+            stage: Pipeline stage where timeout occurred
+        """
+        details: dict[str, Any] = {
+            "timeout_ms": timeout_ms,
+            "elapsed_ms": round(elapsed_ms, 2),
+        }
+        if stage:
+            details["stage"] = stage
+
+        super().__init__(message, details)
+        self.timeout_ms = timeout_ms
+        self.elapsed_ms = elapsed_ms
+        self.stage = stage
+
+
+class ScoringError(ContextError):
+    """
+    Raised when context scoring fails.
+
+    This occurs when relevance, recency, or priority scoring
+    encounters an error.
+    """
+
+    def __init__(
+        self,
+        message: str = "Failed to score context",
+        scorer_type: str | None = None,
+        context_id: str | None = None,
+    ) -> None:
+        """
+        Initialize scoring error.
+
+        Args:
+            message: Error message
+            scorer_type: Type of scorer that failed
+            context_id: ID of context being scored
+        """
+        details: dict[str, Any] = {}
+        if scorer_type:
+            details["scorer_type"] = scorer_type
+        if context_id:
+            details["context_id"] = context_id
+
+        super().__init__(message, details)
+        self.scorer_type = scorer_type
+        self.context_id = context_id
+
+
+class FormattingError(ContextError):
+    """
+    Raised when context formatting fails.
+
+    This occurs when converting assembled context to
+    model-specific format fails.
+    """
+
+    def __init__(
+        self,
+        message: str = "Failed to format context",
+        model: str | None = None,
+        adapter: str | None = None,
+    ) -> None:
+        """
+        Initialize formatting error.
+
+        Args:
+            message: Error message
+            model: Target model
+            adapter: Adapter that failed
+        """
+        details: dict[str, Any] = {}
+        if model:
+            details["model"] = model
+        if adapter:
+            details["adapter"] = adapter
+
+        super().__init__(message, details)
+        self.model = model
+        self.adapter = adapter
+
+
+class CacheError(ContextError):
+    """
+    Raised when cache operations fail.
+
+    This is typically non-fatal and should be handled
+    gracefully by falling back to recomputation.
+    """
+
+    def __init__(
+        self,
+        message: str = "Cache operation failed",
+        operation: str | None = None,
+        cache_key: str | None = None,
+    ) -> None:
+        """
+        Initialize cache error.
+
+        Args:
+            message: Error message
+            operation: Cache operation that failed (get, set, delete)
+            cache_key: Key involved in the failed operation
+        """
+        details: dict[str, Any] = {}
+        if operation:
+            details["operation"] = operation
+        if cache_key:
+            details["cache_key"] = cache_key
+
+        super().__init__(message, details)
+        self.operation = operation
+        self.cache_key = cache_key
+
+
+class ContextNotFoundError(ContextError):
+    """
+    Raised when expected context is not found.
+
+    This occurs when required context sources return
+    no results or are unavailable.
+    """
+
+    def __init__(
+        self,
+        message: str = "Required context not found",
+        source: str | None = None,
+        query: str | None = None,
+    ) -> None:
+        """
+        Initialize context not found error.
+
+        Args:
+            message: Error message
+            source: Source that returned no results
+            query: Query used to search
+        """
+        details: dict[str, Any] = {}
+        if source:
+            details["source"] = source
+        if query:
+            details["query"] = query
+
+        super().__init__(message, details)
+        self.source = source
+        self.query = query
+
+
+class InvalidContextError(ContextError):
+    """
+    Raised when context data is invalid.
+
+    This occurs when context content or metadata
+    fails validation.
+    """
+
+    def __init__(
+        self,
+        message: str = "Invalid context data",
+        field: str | None = None,
+        value: Any | None = None,
+        reason: str | None = None,
+    ) -> None:
+        """
+        Initialize invalid context error.
+
+        Args:
+            message: Error message
+            field: Field that is invalid
+            value: Invalid value (may be redacted for security)
+            reason: Reason for invalidity
+        """
+        details: dict[str, Any] = {}
+        if field:
+            details["field"] = field
+        if value is not None:
+            # Avoid logging potentially sensitive values
+            details["value_type"] = type(value).__name__
+        if reason:
+            details["reason"] = reason
+
+        super().__init__(message, details)
+        self.field = field
+        self.value = value
+        self.reason = reason

backend/app/services/context/prioritization/__init__.py

@@ -0,0 +1,12 @@
+"""
+Context Prioritization Module.
+
+Provides context ranking and selection.
+"""
+
+from .ranker import ContextRanker, RankingResult
+
+__all__ = [
+    "ContextRanker",
+    "RankingResult",
+]

backend/app/services/context/prioritization/ranker.py

@@ -0,0 +1,374 @@
+"""
+Context Ranker for Context Management.
+
+Ranks and selects contexts based on scores and budget constraints.
+"""
+
+import logging
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+from ..budget import TokenBudget, TokenCalculator
+from ..config import ContextSettings, get_context_settings
+from ..exceptions import BudgetExceededError
+from ..scoring.composite import CompositeScorer, ScoredContext
+from ..types import BaseContext, ContextPriority
+
+if TYPE_CHECKING:
+    pass
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class RankingResult:
+    """Result of context ranking and selection."""
+
+    selected: list[ScoredContext]
+    excluded: list[ScoredContext]
+    total_tokens: int
+    selection_stats: dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def selected_contexts(self) -> list[BaseContext]:
+        """Get just the context objects (not scored wrappers)."""
+        return [s.context for s in self.selected]
+
+
+class ContextRanker:
+    """
+    Ranks and selects contexts within budget constraints.
+
+    Uses greedy selection to maximize total score
+    while respecting token budgets per context type.
+    """
+
+    def __init__(
+        self,
+        scorer: CompositeScorer | None = None,
+        calculator: TokenCalculator | None = None,
+        settings: ContextSettings | None = None,
+    ) -> None:
+        """
+        Initialize context ranker.
+
+        Args:
+            scorer: Composite scorer for scoring contexts
+            calculator: Token calculator for counting tokens
+            settings: Context settings (uses global if None)
+        """
+        self._settings = settings or get_context_settings()
+        self._scorer = scorer or CompositeScorer()
+        self._calculator = calculator or TokenCalculator()
+
+    def set_scorer(self, scorer: CompositeScorer) -> None:
+        """Set the scorer."""
+        self._scorer = scorer
+
+    def set_calculator(self, calculator: TokenCalculator) -> None:
+        """Set the token calculator."""
+        self._calculator = calculator
+
+    async def rank(
+        self,
+        contexts: list[BaseContext],
+        query: str,
+        budget: TokenBudget,
+        model: str | None = None,
+        ensure_required: bool = True,
+        **kwargs: Any,
+    ) -> RankingResult:
+        """
+        Rank and select contexts within budget.
+
+        Args:
+            contexts: Contexts to rank
+            query: Query to rank against
+            budget: Token budget constraints
+            model: Model for token counting
+            ensure_required: If True, always include CRITICAL priority contexts
+            **kwargs: Additional scoring parameters
+
+        Returns:
+            RankingResult with selected and excluded contexts
+        """
+        if not contexts:
+            return RankingResult(
+                selected=[],
+                excluded=[],
+                total_tokens=0,
+                selection_stats={"total_contexts": 0},
+            )
+
+        # 1. Ensure all contexts have token counts
+        await self._ensure_token_counts(contexts, model)
+
+        # 2. Score all contexts
+        scored_contexts = await self._scorer.score_batch(contexts, query, **kwargs)
+
+        # 3. Separate required (CRITICAL priority) from optional
+        required: list[ScoredContext] = []
+        optional: list[ScoredContext] = []
+
+        if ensure_required:
+            for sc in scored_contexts:
+                # CRITICAL priority (150) contexts are always included
+                if sc.context.priority >= ContextPriority.CRITICAL.value:
+                    required.append(sc)
+                else:
+                    optional.append(sc)
+        else:
+            optional = list(scored_contexts)
+
+        # 4. Sort optional by score (highest first)
+        optional.sort(reverse=True)
+
+        # 5. Greedy selection
+        selected: list[ScoredContext] = []
+        excluded: list[ScoredContext] = []
+        total_tokens = 0
+
+        # Calculate the usable budget (total minus reserved portions)
+        usable_budget = budget.total - budget.response_reserve - budget.buffer
+
+        # Guard against invalid budget configuration
+        if usable_budget <= 0:
+            raise BudgetExceededError(
+                message=(
+                    f"Invalid budget configuration: no usable tokens available. "
+                    f"total={budget.total}, response_reserve={budget.response_reserve}, "
+                    f"buffer={budget.buffer}"
+                ),
+                allocated=budget.total,
+                requested=0,
+                context_type="CONFIGURATION_ERROR",
+            )
+
+        # First, try to fit required contexts
+        for sc in required:
+            token_count = self._get_valid_token_count(sc.context)
+            context_type = sc.context.get_type()
+
+            if budget.can_fit(context_type, token_count):
+                budget.allocate(context_type, token_count)
+                selected.append(sc)
+                total_tokens += token_count
+            else:
+                # Force-fit CRITICAL contexts if needed, but check total budget first
+                if total_tokens + token_count > usable_budget:
+                    # Even CRITICAL contexts cannot exceed total model context window
+                    raise BudgetExceededError(
+                        message=(
+                            f"CRITICAL contexts exceed total budget. "
+                            f"Context '{sc.context.source}' ({token_count} tokens) "
+                            f"would exceed usable budget of {usable_budget} tokens."
+                        ),
+                        allocated=usable_budget,
+                        requested=total_tokens + token_count,
+                        context_type="CRITICAL_OVERFLOW",
+                    )
+
+                budget.allocate(context_type, token_count, force=True)
+                selected.append(sc)
+                total_tokens += token_count
+                logger.warning(
+                    f"Force-fitted CRITICAL context: {sc.context.source} "
+                    f"({token_count} tokens)"
+                )
+
+        # Then, greedily add optional contexts
+        for sc in optional:
+            token_count = self._get_valid_token_count(sc.context)
+            context_type = sc.context.get_type()
+
+            if budget.can_fit(context_type, token_count):
+                budget.allocate(context_type, token_count)
+                selected.append(sc)
+                total_tokens += token_count
+            else:
+                excluded.append(sc)
+
+        # Build stats
+        stats = {
+            "total_contexts": len(contexts),
+            "required_count": len(required),
+            "selected_count": len(selected),
+            "excluded_count": len(excluded),
+            "total_tokens": total_tokens,
+            "by_type": self._count_by_type(selected),
+        }
+
+        return RankingResult(
+            selected=selected,
+            excluded=excluded,
+            total_tokens=total_tokens,
+            selection_stats=stats,
+        )
+
+    async def rank_simple(
+        self,
+        contexts: list[BaseContext],
+        query: str,
+        max_tokens: int,
+        model: str | None = None,
+        **kwargs: Any,
+    ) -> list[BaseContext]:
+        """
+        Simple ranking without budget per type.
+
+        Selects top contexts by score until max tokens reached.
+
+        Args:
+            contexts: Contexts to rank
+            query: Query to rank against
+            max_tokens: Maximum total tokens
+            model: Model for token counting
+            **kwargs: Additional scoring parameters
+
+        Returns:
+            Selected contexts (in score order)
+        """
+        if not contexts:
+            return []
+
+        # Ensure token counts
+        await self._ensure_token_counts(contexts, model)
+
+        # Score all contexts
+        scored_contexts = await self._scorer.score_batch(contexts, query, **kwargs)
+
+        # Sort by score (highest first)
+        scored_contexts.sort(reverse=True)
+
+        # Greedy selection
+        selected: list[BaseContext] = []
+        total_tokens = 0
+
+        for sc in scored_contexts:
+            token_count = self._get_valid_token_count(sc.context)
+            if total_tokens + token_count <= max_tokens:
+                selected.append(sc.context)
+                total_tokens += token_count
+
+        return selected
+
+    def _get_valid_token_count(self, context: BaseContext) -> int:
+        """
+        Get validated token count from a context.
+
+        Ensures token_count is set (not None) and non-negative to prevent
+        budget bypass attacks where:
+        - None would be treated as 0 (allowing huge contexts to slip through)
+        - Negative values would corrupt budget tracking
+
+        Args:
+            context: Context to get token count from
+
+        Returns:
+            Valid non-negative token count
+
+        Raises:
+            ValueError: If token_count is None or negative
+        """
+        if context.token_count is None:
+            raise ValueError(
+                f"Context '{context.source}' has no token count. "
+                "Ensure _ensure_token_counts() is called before ranking."
+            )
+        if context.token_count < 0:
+            raise ValueError(
+                f"Context '{context.source}' has invalid negative token count: "
+                f"{context.token_count}"
+            )
+        return context.token_count
+
+    async def _ensure_token_counts(
+        self,
+        contexts: list[BaseContext],
+        model: str | None = None,
+    ) -> None:
+        """
+        Ensure all contexts have token counts.
+
+        Counts tokens in parallel for contexts that don't have counts.
+
+        Args:
+            contexts: Contexts to check
+            model: Model for token counting
+        """
+        import asyncio
+
+        # Find contexts needing counts
+        contexts_needing_counts = [ctx for ctx in contexts if ctx.token_count is None]
+
+        if not contexts_needing_counts:
+            return
+
+        # Count all in parallel
+        tasks = [
+            self._calculator.count_tokens(ctx.content, model)
+            for ctx in contexts_needing_counts
+        ]
+        counts = await asyncio.gather(*tasks)
+
+        # Assign counts back
+        for ctx, count in zip(contexts_needing_counts, counts, strict=True):
+            ctx.token_count = count
+
+    def _count_by_type(
+        self, scored_contexts: list[ScoredContext]
+    ) -> dict[str, dict[str, int]]:
+        """Count selected contexts by type."""
+        by_type: dict[str, dict[str, int]] = {}
+
+        for sc in scored_contexts:
+            type_name = sc.context.get_type().value
+            if type_name not in by_type:
+                by_type[type_name] = {"count": 0, "tokens": 0}
+            by_type[type_name]["count"] += 1
+            # Use validated token count (already validated during ranking)
+            by_type[type_name]["tokens"] += sc.context.token_count or 0
+
+        return by_type
+
+    async def rerank_for_diversity(
+        self,
+        scored_contexts: list[ScoredContext],
+        max_per_source: int | None = None,
+    ) -> list[ScoredContext]:
+        """
+        Rerank to ensure source diversity.
+
+        Prevents too many items from the same source.
+
+        Args:
+            scored_contexts: Already scored contexts
+            max_per_source: Maximum items per source (uses settings if None)
+
+        Returns:
+            Reranked contexts
+        """
+        # Use provided value or fall back to settings
+        effective_max = (
+            max_per_source
+            if max_per_source is not None
+            else self._settings.diversity_max_per_source
+        )
+
+        source_counts: dict[str, int] = {}
+        result: list[ScoredContext] = []
+        deferred: list[ScoredContext] = []
+
+        for sc in scored_contexts:
+            source = sc.context.source
+            current_count = source_counts.get(source, 0)
+
+            if current_count < effective_max:
+                result.append(sc)
+                source_counts[source] = current_count + 1
+            else:
+                deferred.append(sc)
+
+        # Add deferred items at the end
+        result.extend(deferred)
+        return result

backend/app/services/context/scoring/__init__.py

@@ -0,0 +1,21 @@
+"""
+Context Scoring Module.
+
+Provides scoring strategies for context prioritization.
+"""
+
+from .base import BaseScorer, ScorerProtocol
+from .composite import CompositeScorer, ScoredContext
+from .priority import PriorityScorer
+from .recency import RecencyScorer
+from .relevance import RelevanceScorer
+
+__all__ = [
+    "BaseScorer",
+    "CompositeScorer",
+    "PriorityScorer",
+    "RecencyScorer",
+    "RelevanceScorer",
+    "ScoredContext",
+    "ScorerProtocol",
+]

backend/app/services/context/scoring/base.py

@@ -0,0 +1,99 @@
+"""
+Base Scorer Protocol and Types.
+
+Defines the interface for context scoring implementations.
+"""
+
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any, Protocol, runtime_checkable
+
+from ..types import BaseContext
+
+if TYPE_CHECKING:
+    pass
+
+
+@runtime_checkable
+class ScorerProtocol(Protocol):
+    """Protocol for context scorers."""
+
+    async def score(
+        self,
+        context: BaseContext,
+        query: str,
+        **kwargs: Any,
+    ) -> float:
+        """
+        Score a context item.
+
+        Args:
+            context: Context to score
+            query: Query to score against
+            **kwargs: Additional scoring parameters
+
+        Returns:
+            Score between 0.0 and 1.0
+        """
+        ...
+
+
+class BaseScorer(ABC):
+    """
+    Abstract base class for context scorers.
+
+    Provides common functionality and interface for
+    different scoring strategies.
+    """
+
+    def __init__(self, weight: float = 1.0) -> None:
+        """
+        Initialize scorer.
+
+        Args:
+            weight: Weight for this scorer in composite scoring
+        """
+        self._weight = weight
+
+    @property
+    def weight(self) -> float:
+        """Get scorer weight."""
+        return self._weight
+
+    @weight.setter
+    def weight(self, value: float) -> None:
+        """Set scorer weight."""
+        if not 0.0 <= value <= 1.0:
+            raise ValueError("Weight must be between 0.0 and 1.0")
+        self._weight = value
+
+    @abstractmethod
+    async def score(
+        self,
+        context: BaseContext,
+        query: str,
+        **kwargs: Any,
+    ) -> float:
+        """
+        Score a context item.
+
+        Args:
+            context: Context to score
+            query: Query to score against
+            **kwargs: Additional scoring parameters
+
+        Returns:
+            Score between 0.0 and 1.0
+        """
+        ...
+
+    def normalize_score(self, score: float) -> float:
+        """
+        Normalize score to [0.0, 1.0] range.
+
+        Args:
+            score: Raw score
+
+        Returns:
+            Normalized score
+        """
+        return max(0.0, min(1.0, score))

backend/app/services/context/scoring/composite.py

@@ -0,0 +1,368 @@
+"""
+Composite Scorer for Context Management.
+
+Combines multiple scoring strategies with configurable weights.
+"""
+
+import asyncio
+import logging
+import time
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+from ..config import ContextSettings, get_context_settings
+from ..types import BaseContext
+from .priority import PriorityScorer
+from .recency import RecencyScorer
+from .relevance import RelevanceScorer
+
+if TYPE_CHECKING:
+    from app.services.mcp.client_manager import MCPClientManager
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ScoredContext:
+    """Context with computed scores."""
+
+    context: BaseContext
+    composite_score: float
+    relevance_score: float = 0.0
+    recency_score: float = 0.0
+    priority_score: float = 0.0
+
+    def __lt__(self, other: "ScoredContext") -> bool:
+        """Enable sorting by composite score."""
+        return self.composite_score < other.composite_score
+
+    def __gt__(self, other: "ScoredContext") -> bool:
+        """Enable sorting by composite score."""
+        return self.composite_score > other.composite_score
+
+
+class CompositeScorer:
+    """
+    Combines multiple scoring strategies.
+
+    Weights:
+    - relevance: How well content matches the query
+    - recency: How recent the content is
+    - priority: Explicit priority assignments
+    """
+
+    def __init__(
+        self,
+        mcp_manager: "MCPClientManager | None" = None,
+        settings: ContextSettings | None = None,
+        relevance_weight: float | None = None,
+        recency_weight: float | None = None,
+        priority_weight: float | None = None,
+    ) -> None:
+        """
+        Initialize composite scorer.
+
+        Args:
+            mcp_manager: MCP manager for semantic scoring
+            settings: Context settings (uses default if None)
+            relevance_weight: Override relevance weight
+            recency_weight: Override recency weight
+            priority_weight: Override priority weight
+        """
+        self._settings = settings or get_context_settings()
+        weights = self._settings.get_scoring_weights()
+
+        self._relevance_weight = (
+            relevance_weight if relevance_weight is not None else weights["relevance"]
+        )
+        self._recency_weight = (
+            recency_weight if recency_weight is not None else weights["recency"]
+        )
+        self._priority_weight = (
+            priority_weight if priority_weight is not None else weights["priority"]
+        )
+
+        # Initialize scorers
+        self._relevance_scorer = RelevanceScorer(
+            mcp_manager=mcp_manager,
+            weight=self._relevance_weight,
+        )
+        self._recency_scorer = RecencyScorer(weight=self._recency_weight)
+        self._priority_scorer = PriorityScorer(weight=self._priority_weight)
+
+        # Per-context locks to prevent race conditions during parallel scoring
+        # Uses dict with (lock, last_used_time) tuples for cleanup
+        self._context_locks: dict[str, tuple[asyncio.Lock, float]] = {}
+        self._locks_lock = asyncio.Lock()  # Lock to protect _context_locks access
+        self._max_locks = 1000  # Maximum locks to keep (prevent memory growth)
+        self._lock_ttl = 60.0  # Seconds before a lock can be cleaned up
+
+    def set_mcp_manager(self, mcp_manager: "MCPClientManager") -> None:
+        """Set MCP manager for semantic scoring."""
+        self._relevance_scorer.set_mcp_manager(mcp_manager)
+
+    @property
+    def weights(self) -> dict[str, float]:
+        """Get current scoring weights."""
+        return {
+            "relevance": self._relevance_weight,
+            "recency": self._recency_weight,
+            "priority": self._priority_weight,
+        }
+
+    def update_weights(
+        self,
+        relevance: float | None = None,
+        recency: float | None = None,
+        priority: float | None = None,
+    ) -> None:
+        """
+        Update scoring weights.
+
+        Args:
+            relevance: New relevance weight
+            recency: New recency weight
+            priority: New priority weight
+        """
+        if relevance is not None:
+            self._relevance_weight = max(0.0, min(1.0, relevance))
+            self._relevance_scorer.weight = self._relevance_weight
+
+        if recency is not None:
+            self._recency_weight = max(0.0, min(1.0, recency))
+            self._recency_scorer.weight = self._recency_weight
+
+        if priority is not None:
+            self._priority_weight = max(0.0, min(1.0, priority))
+            self._priority_scorer.weight = self._priority_weight
+
+    async def _get_context_lock(self, context_id: str) -> asyncio.Lock:
+        """
+        Get or create a lock for a specific context.
+
+        Thread-safe access to per-context locks prevents race conditions
+        when the same context is scored concurrently. Includes automatic
+        cleanup of old locks to prevent memory growth.
+
+        Args:
+            context_id: The context ID to get a lock for
+
+        Returns:
+            asyncio.Lock for the context
+        """
+        now = time.time()
+
+        # Fast path: check if lock exists without acquiring main lock
+        # NOTE: We only READ here - no writes to avoid race conditions
+        # with cleanup. The timestamp will be updated in the slow path
+        # if the lock is still valid.
+        lock_entry = self._context_locks.get(context_id)
+        if lock_entry is not None:
+            lock, _ = lock_entry
+            # Return the lock but defer timestamp update to avoid race
+            # The lock is still valid; timestamp update is best-effort
+            return lock
+
+        # Slow path: create lock or update timestamp while holding main lock
+        async with self._locks_lock:
+            # Double-check after acquiring lock - entry may have been
+            # created by another coroutine or deleted by cleanup
+            lock_entry = self._context_locks.get(context_id)
+            if lock_entry is not None:
+                lock, _ = lock_entry
+                # Safe to update timestamp here since we hold the lock
+                self._context_locks[context_id] = (lock, now)
+                return lock
+
+            # Cleanup old locks if we have too many
+            if len(self._context_locks) >= self._max_locks:
+                self._cleanup_old_locks(now)
+
+            # Create new lock
+            new_lock = asyncio.Lock()
+            self._context_locks[context_id] = (new_lock, now)
+            return new_lock
+
+    def _cleanup_old_locks(self, now: float) -> None:
+        """
+        Remove old locks that haven't been used recently.
+
+        Called while holding _locks_lock. Removes locks older than _lock_ttl,
+        but only if they're not currently held.
+
+        Args:
+            now: Current timestamp for age calculation
+        """
+        cutoff = now - self._lock_ttl
+        to_remove = []
+
+        for context_id, (lock, last_used) in self._context_locks.items():
+            # Only remove if old AND not currently held
+            if last_used < cutoff and not lock.locked():
+                to_remove.append(context_id)
+
+        # Remove oldest 50% if still over limit after TTL filtering
+        if len(self._context_locks) - len(to_remove) >= self._max_locks:
+            # Sort by last used time and mark oldest for removal
+            sorted_entries = sorted(
+                self._context_locks.items(),
+                key=lambda x: x[1][1],  # Sort by last_used time
+            )
+            # Remove oldest 50% that aren't locked
+            target_remove = len(self._context_locks) // 2
+            for context_id, (lock, _) in sorted_entries:
+                if len(to_remove) >= target_remove:
+                    break
+                if context_id not in to_remove and not lock.locked():
+                    to_remove.append(context_id)
+
+        for context_id in to_remove:
+            del self._context_locks[context_id]
+
+        if to_remove:
+            logger.debug(f"Cleaned up {len(to_remove)} context locks")
+
+    async def score(
+        self,
+        context: BaseContext,
+        query: str,
+        **kwargs: Any,
+    ) -> float:
+        """
+        Compute composite score for a context.
+
+        Args:
+            context: Context to score
+            query: Query to score against
+            **kwargs: Additional scoring parameters
+
+        Returns:
+            Composite score between 0.0 and 1.0
+        """
+        scored = await self.score_with_details(context, query, **kwargs)
+        return scored.composite_score
+
+    async def score_with_details(
+        self,
+        context: BaseContext,
+        query: str,
+        **kwargs: Any,
+    ) -> ScoredContext:
+        """
+        Compute composite score with individual scores.
+
+        Uses per-context locking to prevent race conditions when the same
+        context is scored concurrently in parallel scoring operations.
+
+        Args:
+            context: Context to score
+            query: Query to score against
+            **kwargs: Additional scoring parameters
+
+        Returns:
+            ScoredContext with all scores
+        """
+        # Get lock for this specific context to prevent race conditions
+        # within concurrent scoring operations for the same query
+        context_lock = await self._get_context_lock(context.id)
+
+        async with context_lock:
+            # Compute individual scores in parallel
+            # Note: We do NOT cache scores on the context because scores are
+            # query-dependent. Caching without considering the query would
+            # return incorrect scores for different queries.
+            relevance_task = self._relevance_scorer.score(context, query, **kwargs)
+            recency_task = self._recency_scorer.score(context, query, **kwargs)
+            priority_task = self._priority_scorer.score(context, query, **kwargs)
+
+            relevance_score, recency_score, priority_score = await asyncio.gather(
+                relevance_task, recency_task, priority_task
+            )
+
+            # Compute weighted composite
+            total_weight = (
+                self._relevance_weight + self._recency_weight + self._priority_weight
+            )
+
+            if total_weight > 0:
+                composite = (
+                    relevance_score * self._relevance_weight
+                    + recency_score * self._recency_weight
+                    + priority_score * self._priority_weight
+                ) / total_weight
+            else:
+                composite = 0.0
+
+            return ScoredContext(
+                context=context,
+                composite_score=composite,
+                relevance_score=relevance_score,
+                recency_score=recency_score,
+                priority_score=priority_score,
+            )
+
+    async def score_batch(
+        self,
+        contexts: list[BaseContext],
+        query: str,
+        parallel: bool = True,
+        **kwargs: Any,
+    ) -> list[ScoredContext]:
+        """
+        Score multiple contexts.
+
+        Args:
+            contexts: Contexts to score
+            query: Query to score against
+            parallel: Whether to score in parallel
+            **kwargs: Additional scoring parameters
+
+        Returns:
+            List of ScoredContext (same order as input)
+        """
+        if parallel:
+            tasks = [self.score_with_details(ctx, query, **kwargs) for ctx in contexts]
+            return await asyncio.gather(*tasks)
+        else:
+            results = []
+            for ctx in contexts:
+                scored = await self.score_with_details(ctx, query, **kwargs)
+                results.append(scored)
+            return results
+
+    async def rank(
+        self,
+        contexts: list[BaseContext],
+        query: str,
+        limit: int | None = None,
+        min_score: float = 0.0,
+        **kwargs: Any,
+    ) -> list[ScoredContext]:
+        """
+        Score and rank contexts.
+
+        Args:
+            contexts: Contexts to rank
+            query: Query to rank against
+            limit: Maximum number of results
+            min_score: Minimum score threshold
+            **kwargs: Additional scoring parameters
+
+        Returns:
+            Sorted list of ScoredContext (highest first)
+        """
+        # Score all contexts
+        scored = await self.score_batch(contexts, query, **kwargs)
+
+        # Filter by minimum score
+        if min_score > 0:
+            scored = [s for s in scored if s.composite_score >= min_score]
+
+        # Sort by score (highest first)
+        scored.sort(reverse=True)
+
+        # Apply limit
+        if limit is not None:
+            scored = scored[:limit]
+
+        return scored

backend/app/services/context/scoring/priority.py

@@ -0,0 +1,135 @@
+"""
+Priority Scorer for Context Management.
+
+Scores context based on assigned priority levels.
+"""
+
+from typing import Any, ClassVar
+
+from ..types import BaseContext, ContextType
+from .base import BaseScorer
+
+
+class PriorityScorer(BaseScorer):
+    """
+    Scores context based on priority levels.
+
+    Converts priority enum values to normalized scores.
+    Also applies type-based priority bonuses.
+    """
+
+    # Default priority bonuses by context type
+    DEFAULT_TYPE_BONUSES: ClassVar[dict[ContextType, float]] = {
+        ContextType.SYSTEM: 0.2,  # System prompts get a boost
+        ContextType.TASK: 0.15,  # Current task is important
+        ContextType.TOOL: 0.1,  # Recent tool results matter
+        ContextType.KNOWLEDGE: 0.0,  # Knowledge scored by relevance
+        ContextType.CONVERSATION: 0.0,  # Conversation scored by recency
+    }
+
+    def __init__(
+        self,
+        weight: float = 1.0,
+        type_bonuses: dict[ContextType, float] | None = None,
+    ) -> None:
+        """
+        Initialize priority scorer.
+
+        Args:
+            weight: Scorer weight for composite scoring
+            type_bonuses: Optional context-type priority bonuses
+        """
+        super().__init__(weight)
+        self._type_bonuses = type_bonuses or self.DEFAULT_TYPE_BONUSES.copy()
+
+    async def score(
+        self,
+        context: BaseContext,
+        query: str,
+        **kwargs: Any,
+    ) -> float:
+        """
+        Score context based on priority.
+
+        Args:
+            context: Context to score
+            query: Query (not used for priority, kept for interface)
+            **kwargs: Additional parameters
+
+        Returns:
+            Priority score between 0.0 and 1.0
+        """
+        # Get base priority score
+        priority_value = context.priority
+        base_score = self._priority_to_score(priority_value)
+
+        # Apply type bonus
+        context_type = context.get_type()
+        bonus = self._type_bonuses.get(context_type, 0.0)
+
+        return self.normalize_score(base_score + bonus)
+
+    def _priority_to_score(self, priority: int) -> float:
+        """
+        Convert priority value to normalized score.
+
+        Priority values (from ContextPriority):
+        - CRITICAL (100) -> 1.0
+        - HIGH (80) -> 0.8
+        - NORMAL (50) -> 0.5
+        - LOW (20) -> 0.2
+        - MINIMAL (0) -> 0.0
+
+        Args:
+            priority: Priority value (0-100)
+
+        Returns:
+            Normalized score (0.0-1.0)
+        """
+        # Clamp to valid range
+        clamped = max(0, min(100, priority))
+        return clamped / 100.0
+
+    def get_type_bonus(self, context_type: ContextType) -> float:
+        """
+        Get priority bonus for a context type.
+
+        Args:
+            context_type: Context type
+
+        Returns:
+            Bonus value
+        """
+        return self._type_bonuses.get(context_type, 0.0)
+
+    def set_type_bonus(self, context_type: ContextType, bonus: float) -> None:
+        """
+        Set priority bonus for a context type.
+
+        Args:
+            context_type: Context type
+            bonus: Bonus value (0.0-1.0)
+        """
+        if not 0.0 <= bonus <= 1.0:
+            raise ValueError("Bonus must be between 0.0 and 1.0")
+        self._type_bonuses[context_type] = bonus
+
+    async def score_batch(
+        self,
+        contexts: list[BaseContext],
+        query: str,
+        **kwargs: Any,
+    ) -> list[float]:
+        """
+        Score multiple contexts.
+
+        Args:
+            contexts: Contexts to score
+            query: Query (not used)
+            **kwargs: Additional parameters
+
+        Returns:
+            List of scores (same order as input)
+        """
+        # Priority scoring is fast, no async needed
+        return [await self.score(ctx, query, **kwargs) for ctx in contexts]

backend/app/services/context/scoring/recency.py

@@ -0,0 +1,141 @@
+"""
+Recency Scorer for Context Management.
+
+Scores context based on how recent it is.
+More recent content gets higher scores.
+"""
+
+import math
+from datetime import UTC, datetime
+from typing import Any
+
+from ..types import BaseContext, ContextType
+from .base import BaseScorer
+
+
+class RecencyScorer(BaseScorer):
+    """
+    Scores context based on recency.
+
+    Uses exponential decay to score content based on age.
+    More recent content scores higher.
+    """
+
+    def __init__(
+        self,
+        weight: float = 1.0,
+        half_life_hours: float = 24.0,
+        type_half_lives: dict[ContextType, float] | None = None,
+    ) -> None:
+        """
+        Initialize recency scorer.
+
+        Args:
+            weight: Scorer weight for composite scoring
+            half_life_hours: Default hours until score decays to 0.5
+            type_half_lives: Optional context-type-specific half lives
+        """
+        super().__init__(weight)
+        self._half_life_hours = half_life_hours
+        self._type_half_lives = type_half_lives or {}
+
+        # Set sensible defaults for context types
+        if ContextType.CONVERSATION not in self._type_half_lives:
+            self._type_half_lives[ContextType.CONVERSATION] = 1.0  # 1 hour
+        if ContextType.TOOL not in self._type_half_lives:
+            self._type_half_lives[ContextType.TOOL] = 0.5  # 30 minutes
+        if ContextType.KNOWLEDGE not in self._type_half_lives:
+            self._type_half_lives[ContextType.KNOWLEDGE] = 168.0  # 1 week
+        if ContextType.SYSTEM not in self._type_half_lives:
+            self._type_half_lives[ContextType.SYSTEM] = 720.0  # 30 days
+        if ContextType.TASK not in self._type_half_lives:
+            self._type_half_lives[ContextType.TASK] = 24.0  # 1 day
+
+    async def score(
+        self,
+        context: BaseContext,
+        query: str,
+        **kwargs: Any,
+    ) -> float:
+        """
+        Score context based on recency.
+
+        Args:
+            context: Context to score
+            query: Query (not used for recency, kept for interface)
+            **kwargs: Additional parameters
+                - reference_time: Time to measure recency from (default: now)
+
+        Returns:
+            Recency score between 0.0 and 1.0
+        """
+        reference_time = kwargs.get("reference_time")
+        if reference_time is None:
+            reference_time = datetime.now(UTC)
+        elif reference_time.tzinfo is None:
+            reference_time = reference_time.replace(tzinfo=UTC)
+
+        # Ensure context timestamp is timezone-aware
+        context_time = context.timestamp
+        if context_time.tzinfo is None:
+            context_time = context_time.replace(tzinfo=UTC)
+
+        # Calculate age in hours
+        age = reference_time - context_time
+        age_hours = max(0, age.total_seconds() / 3600)
+
+        # Get half-life for this context type
+        context_type = context.get_type()
+        half_life = self._type_half_lives.get(context_type, self._half_life_hours)
+
+        # Exponential decay
+        decay_factor = math.exp(-math.log(2) * age_hours / half_life)
+
+        return self.normalize_score(decay_factor)
+
+    def get_half_life(self, context_type: ContextType) -> float:
+        """
+        Get half-life for a context type.
+
+        Args:
+            context_type: Context type to get half-life for
+
+        Returns:
+            Half-life in hours
+        """
+        return self._type_half_lives.get(context_type, self._half_life_hours)
+
+    def set_half_life(self, context_type: ContextType, hours: float) -> None:
+        """
+        Set half-life for a context type.
+
+        Args:
+            context_type: Context type to set half-life for
+            hours: Half-life in hours
+        """
+        if hours <= 0:
+            raise ValueError("Half-life must be positive")
+        self._type_half_lives[context_type] = hours
+
+    async def score_batch(
+        self,
+        contexts: list[BaseContext],
+        query: str,
+        **kwargs: Any,
+    ) -> list[float]:
+        """
+        Score multiple contexts.
+
+        Args:
+            contexts: Contexts to score
+            query: Query (not used)
+            **kwargs: Additional parameters
+
+        Returns:
+            List of scores (same order as input)
+        """
+        scores = []
+        for context in contexts:
+            score = await self.score(context, query, **kwargs)
+            scores.append(score)
+        return scores

backend/app/services/context/scoring/relevance.py

@@ -0,0 +1,220 @@
+"""
+Relevance Scorer for Context Management.
+
+Scores context based on semantic similarity to the query.
+Uses Knowledge Base embeddings when available.
+"""
+
+import logging
+import re
+from typing import TYPE_CHECKING, Any
+
+from ..config import ContextSettings, get_context_settings
+from ..types import BaseContext, KnowledgeContext
+from .base import BaseScorer
+
+if TYPE_CHECKING:
+    from app.services.mcp.client_manager import MCPClientManager
+
+logger = logging.getLogger(__name__)
+
+
+class RelevanceScorer(BaseScorer):
+    """
+    Scores context based on relevance to query.
+
+    Uses multiple strategies:
+    1. Pre-computed scores (from RAG results)
+    2. MCP-based semantic similarity (via Knowledge Base)
+    3. Keyword matching fallback
+    """
+
+    def __init__(
+        self,
+        mcp_manager: "MCPClientManager | None" = None,
+        weight: float = 1.0,
+        keyword_fallback_weight: float | None = None,
+        semantic_max_chars: int | None = None,
+        settings: ContextSettings | None = None,
+    ) -> None:
+        """
+        Initialize relevance scorer.
+
+        Args:
+            mcp_manager: MCP manager for Knowledge Base calls
+            weight: Scorer weight for composite scoring
+            keyword_fallback_weight: Max score for keyword-based fallback (overrides settings)
+            semantic_max_chars: Max content length for semantic similarity (overrides settings)
+            settings: Context settings (uses global if None)
+        """
+        super().__init__(weight)
+        self._settings = settings or get_context_settings()
+        self._mcp = mcp_manager
+
+        # Use provided values or fall back to settings
+        self._keyword_fallback_weight = (
+            keyword_fallback_weight
+            if keyword_fallback_weight is not None
+            else self._settings.relevance_keyword_fallback_weight
+        )
+        self._semantic_max_chars = (
+            semantic_max_chars
+            if semantic_max_chars is not None
+            else self._settings.relevance_semantic_max_chars
+        )
+
+    def set_mcp_manager(self, mcp_manager: "MCPClientManager") -> None:
+        """Set MCP manager for semantic scoring."""
+        self._mcp = mcp_manager
+
+    async def score(
+        self,
+        context: BaseContext,
+        query: str,
+        **kwargs: Any,
+    ) -> float:
+        """
+        Score context relevance to query.
+
+        Args:
+            context: Context to score
+            query: Query to score against
+            **kwargs: Additional parameters
+
+        Returns:
+            Relevance score between 0.0 and 1.0
+        """
+        # 1. Check for pre-computed relevance score
+        if (
+            isinstance(context, KnowledgeContext)
+            and context.relevance_score is not None
+        ):
+            return self.normalize_score(context.relevance_score)
+
+        # 2. Check metadata for score
+        if "relevance_score" in context.metadata:
+            return self.normalize_score(context.metadata["relevance_score"])
+
+        if "score" in context.metadata:
+            return self.normalize_score(context.metadata["score"])
+
+        # 3. Try MCP-based semantic similarity (if compute_similarity tool is available)
+        # Note: This requires the knowledge-base MCP server to implement compute_similarity
+        if self._mcp is not None:
+            try:
+                score = await self._compute_semantic_similarity(context, query)
+                if score is not None:
+                    return score
+            except Exception as e:
+                # Log at debug level since this is expected if compute_similarity
+                # tool is not implemented in the Knowledge Base server
+                logger.debug(
+                    f"Semantic scoring unavailable, using keyword fallback: {e}"
+                )
+
+        # 4. Fall back to keyword matching
+        return self._compute_keyword_score(context, query)
+
+    async def _compute_semantic_similarity(
+        self,
+        context: BaseContext,
+        query: str,
+    ) -> float | None:
+        """
+        Compute semantic similarity using Knowledge Base embeddings.
+
+        Args:
+            context: Context to score
+            query: Query to compare
+
+        Returns:
+            Similarity score or None if unavailable
+        """
+        if self._mcp is None:
+            return None
+
+        try:
+            # Use Knowledge Base's search capability to compute similarity
+            result = await self._mcp.call_tool(
+                server="knowledge-base",
+                tool="compute_similarity",
+                args={
+                    "text1": query,
+                    "text2": context.content[
+                        : self._semantic_max_chars
+                    ],  # Limit content length
+                },
+            )
+
+            if result.success and isinstance(result.data, dict):
+                similarity = result.data.get("similarity")
+                if similarity is not None:
+                    return self.normalize_score(float(similarity))
+
+        except Exception as e:
+            logger.debug(f"Semantic similarity computation failed: {e}")
+
+        return None
+
+    def _compute_keyword_score(
+        self,
+        context: BaseContext,
+        query: str,
+    ) -> float:
+        """
+        Compute relevance score based on keyword matching.
+
+        Simple but fast fallback when semantic search is unavailable.
+
+        Args:
+            context: Context to score
+            query: Query to match
+
+        Returns:
+            Keyword-based relevance score
+        """
+        if not query or not context.content:
+            return 0.0
+
+        # Extract keywords from query
+        query_lower = query.lower()
+        content_lower = context.content.lower()
+
+        # Simple word tokenization
+        query_words = set(re.findall(r"\b\w{3,}\b", query_lower))
+        content_words = set(re.findall(r"\b\w{3,}\b", content_lower))
+
+        if not query_words:
+            return 0.0
+
+        # Calculate overlap
+        common_words = query_words & content_words
+        overlap_ratio = len(common_words) / len(query_words)
+
+        # Apply fallback weight ceiling
+        return self.normalize_score(overlap_ratio * self._keyword_fallback_weight)
+
+    async def score_batch(
+        self,
+        contexts: list[BaseContext],
+        query: str,
+        **kwargs: Any,
+    ) -> list[float]:
+        """
+        Score multiple contexts in parallel.
+
+        Args:
+            contexts: Contexts to score
+            query: Query to score against
+            **kwargs: Additional parameters
+
+        Returns:
+            List of scores (same order as input)
+        """
+        import asyncio
+
+        if not contexts:
+            return []
+
+        tasks = [self.score(context, query, **kwargs) for context in contexts]
+        return await asyncio.gather(*tasks)

backend/app/services/context/types/__init__.py

@@ -0,0 +1,49 @@
+"""
+Context Types Module.
+
+Provides all context types used in the Context Management Engine.
+"""
+
+from .base import (
+    AssembledContext,
+    BaseContext,
+    ContextPriority,
+    ContextType,
+)
+from .conversation import (
+    ConversationContext,
+    MessageRole,
+)
+from .knowledge import KnowledgeContext
+from .memory import (
+    MemoryContext,
+    MemorySubtype,
+)
+from .system import SystemContext
+from .task import (
+    TaskComplexity,
+    TaskContext,
+    TaskStatus,
+)
+from .tool import (
+    ToolContext,
+    ToolResultStatus,
+)
+
+__all__ = [
+    "AssembledContext",
+    "BaseContext",
+    "ContextPriority",
+    "ContextType",
+    "ConversationContext",
+    "KnowledgeContext",
+    "MemoryContext",
+    "MemorySubtype",
+    "MessageRole",
+    "SystemContext",
+    "TaskComplexity",
+    "TaskContext",
+    "TaskStatus",
+    "ToolContext",
+    "ToolResultStatus",
+]

backend/app/services/context/types/base.py

@@ -0,0 +1,348 @@
+"""
+Base Context Types and Enums.
+
+Provides the foundation for all context types used in
+the Context Management Engine.
+"""
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from enum import Enum
+from typing import Any
+from uuid import uuid4
+
+
+class ContextType(str, Enum):
+    """
+    Types of context that can be assembled.
+
+    Each type has specific handling, formatting, and
+    budget allocation rules.
+    """
+
+    SYSTEM = "system"
+    TASK = "task"
+    KNOWLEDGE = "knowledge"
+    CONVERSATION = "conversation"
+    TOOL = "tool"
+    MEMORY = "memory"  # Agent memory (working, episodic, semantic, procedural)
+
+    @classmethod
+    def from_string(cls, value: str) -> "ContextType":
+        """
+        Convert string to ContextType.
+
+        Args:
+            value: String value
+
+        Returns:
+            ContextType enum value
+
+        Raises:
+            ValueError: If value is not a valid context type
+        """
+        try:
+            return cls(value.lower())
+        except ValueError:
+            valid = ", ".join(t.value for t in cls)
+            raise ValueError(f"Invalid context type '{value}'. Valid types: {valid}")
+
+
+class ContextPriority(int, Enum):
+    """
+    Priority levels for context ordering.
+
+    Higher values indicate higher priority.
+    """
+
+    LOWEST = 0
+    LOW = 25
+    NORMAL = 50
+    HIGH = 75
+    HIGHEST = 100
+    CRITICAL = 150  # Never omit
+
+    @classmethod
+    def from_int(cls, value: int) -> "ContextPriority":
+        """
+        Get closest priority level for an integer.
+
+        Args:
+            value: Integer priority value
+
+        Returns:
+            Closest ContextPriority enum value
+        """
+        priorities = sorted(cls, key=lambda p: p.value)
+        for priority in reversed(priorities):
+            if value >= priority.value:
+                return priority
+        return cls.LOWEST
+
+
+@dataclass(eq=False)
+class BaseContext(ABC):
+    """
+    Abstract base class for all context types.
+
+    Provides common fields and methods for context handling,
+    scoring, and serialization.
+    """
+
+    # Required fields
+    content: str
+    source: str
+
+    # Optional fields with defaults
+    id: str = field(default_factory=lambda: str(uuid4()))
+    timestamp: datetime = field(default_factory=lambda: datetime.now(UTC))
+    priority: int = field(default=ContextPriority.NORMAL.value)
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    # Computed/cached fields
+    _token_count: int | None = field(default=None, repr=False)
+    _score: float | None = field(default=None, repr=False)
+
+    @property
+    def token_count(self) -> int | None:
+        """Get cached token count (None if not counted yet)."""
+        return self._token_count
+
+    @token_count.setter
+    def token_count(self, value: int) -> None:
+        """Set token count."""
+        self._token_count = value
+
+    @property
+    def score(self) -> float | None:
+        """Get cached score (None if not scored yet)."""
+        return self._score
+
+    @score.setter
+    def score(self, value: float) -> None:
+        """Set score (clamped to 0.0-1.0)."""
+        self._score = max(0.0, min(1.0, value))
+
+    @abstractmethod
+    def get_type(self) -> ContextType:
+        """
+        Get the type of this context.
+
+        Returns:
+            ContextType enum value
+        """
+        ...
+
+    def get_age_seconds(self) -> float:
+        """
+        Get age of context in seconds.
+
+        Returns:
+            Age in seconds since creation
+        """
+        now = datetime.now(UTC)
+        delta = now - self.timestamp
+        return delta.total_seconds()
+
+    def get_age_hours(self) -> float:
+        """
+        Get age of context in hours.
+
+        Returns:
+            Age in hours since creation
+        """
+        return self.get_age_seconds() / 3600
+
+    def is_stale(self, max_age_hours: float = 168.0) -> bool:
+        """
+        Check if context is stale.
+
+        Args:
+            max_age_hours: Maximum age before considered stale (default 7 days)
+
+        Returns:
+            True if context is older than max_age_hours
+        """
+        return self.get_age_hours() > max_age_hours
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert context to dictionary for serialization.
+
+        Returns:
+            Dictionary representation
+        """
+        return {
+            "id": self.id,
+            "type": self.get_type().value,
+            "content": self.content,
+            "source": self.source,
+            "timestamp": self.timestamp.isoformat(),
+            "priority": self.priority,
+            "metadata": self.metadata,
+            "token_count": self._token_count,
+            "score": self._score,
+        }
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "BaseContext":
+        """
+        Create context from dictionary.
+
+        Note: Subclasses should override this to return correct type.
+
+        Args:
+            data: Dictionary with context data
+
+        Returns:
+            Context instance
+        """
+        raise NotImplementedError("Subclasses must implement from_dict")
+
+    def truncate(self, max_tokens: int, suffix: str = "... [truncated]") -> str:
+        """
+        Truncate content to fit within token limit.
+
+        This is a rough estimation based on characters.
+        For accurate truncation, use the TokenCalculator.
+
+        Args:
+            max_tokens: Maximum tokens allowed
+            suffix: Suffix to append when truncated
+
+        Returns:
+            Truncated content
+        """
+        if self._token_count is None or self._token_count <= max_tokens:
+            return self.content
+
+        # Rough estimation: 4 chars per token on average
+        estimated_chars = max_tokens * 4
+        suffix_chars = len(suffix)
+
+        if len(self.content) <= estimated_chars:
+            return self.content
+
+        truncated = self.content[: estimated_chars - suffix_chars]
+        # Try to break at word boundary
+        last_space = truncated.rfind(" ")
+        if last_space > estimated_chars * 0.8:
+            truncated = truncated[:last_space]
+
+        return truncated + suffix
+
+    def __hash__(self) -> int:
+        """Hash based on ID for set/dict usage."""
+        return hash(self.id)
+
+    def __eq__(self, other: object) -> bool:
+        """Equality based on ID."""
+        if not isinstance(other, BaseContext):
+            return False
+        return self.id == other.id
+
+
+@dataclass
+class AssembledContext:
+    """
+    Result of context assembly.
+
+    Contains the final formatted context ready for LLM consumption,
+    along with metadata about the assembly process.
+    """
+
+    # Main content
+    content: str
+    total_tokens: int
+
+    # Assembly metadata
+    context_count: int
+    excluded_count: int = 0
+    assembly_time_ms: float = 0.0
+    model: str = ""
+
+    # Included contexts (optional - for inspection)
+    contexts: list["BaseContext"] = field(default_factory=list)
+
+    # Additional metadata from assembly
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    # Budget tracking
+    budget_total: int = 0
+    budget_used: int = 0
+
+    # Context breakdown
+    by_type: dict[str, int] = field(default_factory=dict)
+
+    # Cache info
+    cache_hit: bool = False
+    cache_key: str | None = None
+
+    # Aliases for backward compatibility
+    @property
+    def token_count(self) -> int:
+        """Alias for total_tokens."""
+        return self.total_tokens
+
+    @property
+    def contexts_included(self) -> int:
+        """Alias for context_count."""
+        return self.context_count
+
+    @property
+    def contexts_excluded(self) -> int:
+        """Alias for excluded_count."""
+        return self.excluded_count
+
+    @property
+    def budget_utilization(self) -> float:
+        """Get budget utilization percentage."""
+        if self.budget_total == 0:
+            return 0.0
+        return self.budget_used / self.budget_total
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "content": self.content,
+            "total_tokens": self.total_tokens,
+            "context_count": self.context_count,
+            "excluded_count": self.excluded_count,
+            "assembly_time_ms": round(self.assembly_time_ms, 2),
+            "model": self.model,
+            "metadata": self.metadata,
+            "budget_total": self.budget_total,
+            "budget_used": self.budget_used,
+            "budget_utilization": round(self.budget_utilization, 3),
+            "by_type": self.by_type,
+            "cache_hit": self.cache_hit,
+            "cache_key": self.cache_key,
+        }
+
+    def to_json(self) -> str:
+        """Convert to JSON string."""
+        import json
+
+        return json.dumps(self.to_dict())
+
+    @classmethod
+    def from_json(cls, json_str: str) -> "AssembledContext":
+        """Create from JSON string."""
+        import json
+
+        data = json.loads(json_str)
+        return cls(
+            content=data["content"],
+            total_tokens=data["total_tokens"],
+            context_count=data["context_count"],
+            excluded_count=data.get("excluded_count", 0),
+            assembly_time_ms=data.get("assembly_time_ms", 0.0),
+            model=data.get("model", ""),
+            metadata=data.get("metadata", {}),
+            budget_total=data.get("budget_total", 0),
+            budget_used=data.get("budget_used", 0),
+            by_type=data.get("by_type", {}),
+            cache_hit=data.get("cache_hit", False),
+            cache_key=data.get("cache_key"),
+        )

backend/app/services/context/types/conversation.py

@@ -0,0 +1,182 @@
+"""
+Conversation Context Type.
+
+Represents conversation history for context continuity.
+"""
+
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from enum import Enum
+from typing import Any
+
+from .base import BaseContext, ContextPriority, ContextType
+
+
+class MessageRole(str, Enum):
+    """Roles for conversation messages."""
+
+    USER = "user"
+    ASSISTANT = "assistant"
+    SYSTEM = "system"
+    TOOL = "tool"
+
+    @classmethod
+    def from_string(cls, value: str) -> "MessageRole":
+        """Convert string to MessageRole."""
+        try:
+            return cls(value.lower())
+        except ValueError:
+            # Default to user for unknown roles
+            return cls.USER
+
+
+@dataclass(eq=False)
+class ConversationContext(BaseContext):
+    """
+    Context from conversation history.
+
+    Represents a single turn in the conversation,
+    including user messages, assistant responses,
+    and tool results.
+    """
+
+    # Conversation-specific fields
+    role: MessageRole = field(default=MessageRole.USER)
+    turn_index: int = field(default=0)
+    session_id: str | None = field(default=None)
+    parent_message_id: str | None = field(default=None)
+
+    def get_type(self) -> ContextType:
+        """Return CONVERSATION context type."""
+        return ContextType.CONVERSATION
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary with conversation-specific fields."""
+        base = super().to_dict()
+        base.update(
+            {
+                "role": self.role.value,
+                "turn_index": self.turn_index,
+                "session_id": self.session_id,
+                "parent_message_id": self.parent_message_id,
+            }
+        )
+        return base
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "ConversationContext":
+        """Create ConversationContext from dictionary."""
+        role = data.get("role", "user")
+        if isinstance(role, str):
+            role = MessageRole.from_string(role)
+
+        return cls(
+            id=data.get("id", ""),
+            content=data["content"],
+            source=data.get("source", "conversation"),
+            timestamp=datetime.fromisoformat(data["timestamp"])
+            if isinstance(data.get("timestamp"), str)
+            else data.get("timestamp", datetime.now(UTC)),
+            priority=data.get("priority", ContextPriority.NORMAL.value),
+            metadata=data.get("metadata", {}),
+            role=role,
+            turn_index=data.get("turn_index", 0),
+            session_id=data.get("session_id"),
+            parent_message_id=data.get("parent_message_id"),
+        )
+
+    @classmethod
+    def from_message(
+        cls,
+        content: str,
+        role: str | MessageRole,
+        turn_index: int = 0,
+        session_id: str | None = None,
+        timestamp: datetime | None = None,
+    ) -> "ConversationContext":
+        """
+        Create ConversationContext from a message.
+
+        Args:
+            content: Message content
+            role: Message role (user, assistant, system, tool)
+            turn_index: Position in conversation
+            session_id: Session identifier
+            timestamp: Message timestamp
+
+        Returns:
+            ConversationContext instance
+        """
+        if isinstance(role, str):
+            role = MessageRole.from_string(role)
+
+        # Recent messages have higher priority
+        priority = ContextPriority.NORMAL.value
+
+        return cls(
+            content=content,
+            source="conversation",
+            role=role,
+            turn_index=turn_index,
+            session_id=session_id,
+            timestamp=timestamp or datetime.now(UTC),
+            priority=priority,
+        )
+
+    @classmethod
+    def from_history(
+        cls,
+        messages: list[dict[str, Any]],
+        session_id: str | None = None,
+    ) -> list["ConversationContext"]:
+        """
+        Create multiple ConversationContexts from message history.
+
+        Args:
+            messages: List of message dicts with 'role' and 'content'
+            session_id: Session identifier
+
+        Returns:
+            List of ConversationContext instances
+        """
+        contexts = []
+        for i, msg in enumerate(messages):
+            ctx = cls.from_message(
+                content=msg.get("content", ""),
+                role=msg.get("role", "user"),
+                turn_index=i,
+                session_id=session_id,
+                timestamp=datetime.fromisoformat(msg["timestamp"])
+                if "timestamp" in msg
+                else None,
+            )
+            contexts.append(ctx)
+        return contexts
+
+    def is_user_message(self) -> bool:
+        """Check if this is a user message."""
+        return self.role == MessageRole.USER
+
+    def is_assistant_message(self) -> bool:
+        """Check if this is an assistant message."""
+        return self.role == MessageRole.ASSISTANT
+
+    def is_tool_result(self) -> bool:
+        """Check if this is a tool result."""
+        return self.role == MessageRole.TOOL
+
+    def format_for_prompt(self) -> str:
+        """
+        Format message for inclusion in prompt.
+
+        Returns:
+            Formatted message string
+        """
+        role_labels = {
+            MessageRole.USER: "User",
+            MessageRole.ASSISTANT: "Assistant",
+            MessageRole.SYSTEM: "System",
+            MessageRole.TOOL: "Tool Result",
+        }
+        label = role_labels.get(self.role, "Unknown")
+        return f"{label}: {self.content}"

backend/app/services/context/types/knowledge.py

@@ -0,0 +1,152 @@
+"""
+Knowledge Context Type.
+
+Represents RAG results from the Knowledge Base MCP server.
+"""
+
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from typing import Any
+
+from .base import BaseContext, ContextPriority, ContextType
+
+
+@dataclass(eq=False)
+class KnowledgeContext(BaseContext):
+    """
+    Context from knowledge base / RAG retrieval.
+
+    Knowledge context represents chunks retrieved from the
+    Knowledge Base MCP server, including:
+    - Code snippets
+    - Documentation
+    - Previous conversations
+    - External knowledge
+
+    Each chunk includes relevance scoring from the search.
+    """
+
+    # Knowledge-specific fields
+    collection: str = field(default="default")
+    file_type: str | None = field(default=None)
+    chunk_index: int = field(default=0)
+    relevance_score: float = field(default=0.0)
+    search_query: str = field(default="")
+
+    def get_type(self) -> ContextType:
+        """Return KNOWLEDGE context type."""
+        return ContextType.KNOWLEDGE
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary with knowledge-specific fields."""
+        base = super().to_dict()
+        base.update(
+            {
+                "collection": self.collection,
+                "file_type": self.file_type,
+                "chunk_index": self.chunk_index,
+                "relevance_score": self.relevance_score,
+                "search_query": self.search_query,
+            }
+        )
+        return base
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "KnowledgeContext":
+        """Create KnowledgeContext from dictionary."""
+        return cls(
+            id=data.get("id", ""),
+            content=data["content"],
+            source=data["source"],
+            timestamp=datetime.fromisoformat(data["timestamp"])
+            if isinstance(data.get("timestamp"), str)
+            else data.get("timestamp", datetime.now(UTC)),
+            priority=data.get("priority", ContextPriority.NORMAL.value),
+            metadata=data.get("metadata", {}),
+            collection=data.get("collection", "default"),
+            file_type=data.get("file_type"),
+            chunk_index=data.get("chunk_index", 0),
+            relevance_score=data.get("relevance_score", 0.0),
+            search_query=data.get("search_query", ""),
+        )
+
+    @classmethod
+    def from_search_result(
+        cls,
+        result: dict[str, Any],
+        query: str,
+    ) -> "KnowledgeContext":
+        """
+        Create KnowledgeContext from a Knowledge Base search result.
+
+        Args:
+            result: Search result from Knowledge Base MCP
+            query: Search query used
+
+        Returns:
+            KnowledgeContext instance
+        """
+        return cls(
+            content=result.get("content", ""),
+            source=result.get("source_path", "unknown"),
+            collection=result.get("collection", "default"),
+            file_type=result.get("file_type"),
+            chunk_index=result.get("chunk_index", 0),
+            relevance_score=result.get("score", 0.0),
+            search_query=query,
+            metadata={
+                "chunk_id": result.get("id"),
+                "content_hash": result.get("content_hash"),
+            },
+        )
+
+    @classmethod
+    def from_search_results(
+        cls,
+        results: list[dict[str, Any]],
+        query: str,
+    ) -> list["KnowledgeContext"]:
+        """
+        Create multiple KnowledgeContexts from search results.
+
+        Args:
+            results: List of search results
+            query: Search query used
+
+        Returns:
+            List of KnowledgeContext instances
+        """
+        return [cls.from_search_result(r, query) for r in results]
+
+    def is_code(self) -> bool:
+        """Check if this is code content."""
+        code_types = {
+            "python",
+            "javascript",
+            "typescript",
+            "go",
+            "rust",
+            "java",
+            "c",
+            "cpp",
+        }
+        return self.file_type is not None and self.file_type.lower() in code_types
+
+    def is_documentation(self) -> bool:
+        """Check if this is documentation content."""
+        doc_types = {"markdown", "rst", "txt", "md"}
+        return self.file_type is not None and self.file_type.lower() in doc_types
+
+    def get_formatted_source(self) -> str:
+        """
+        Get a formatted source string for display.
+
+        Returns:
+            Formatted source string
+        """
+        parts = [self.source]
+        if self.file_type:
+            parts.append(f"({self.file_type})")
+        if self.collection != "default":
+            parts.insert(0, f"[{self.collection}]")
+        return " ".join(parts)

backend/app/services/context/types/memory.py

@@ -0,0 +1,282 @@
+"""
+Memory Context Type.
+
+Represents agent memory as context for LLM requests.
+Includes working, episodic, semantic, and procedural memories.
+"""
+
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from enum import Enum
+from typing import Any
+
+from .base import BaseContext, ContextPriority, ContextType
+
+
+class MemorySubtype(str, Enum):
+    """Types of agent memory."""
+
+    WORKING = "working"  # Session-scoped temporary data
+    EPISODIC = "episodic"  # Task history and outcomes
+    SEMANTIC = "semantic"  # Facts and knowledge
+    PROCEDURAL = "procedural"  # Learned procedures
+
+
+@dataclass(eq=False)
+class MemoryContext(BaseContext):
+    """
+    Context from agent memory system.
+
+    Memory context represents data retrieved from the agent
+    memory system, including:
+    - Working memory: Current session state
+    - Episodic memory: Past task experiences
+    - Semantic memory: Learned facts and knowledge
+    - Procedural memory: Known procedures and workflows
+
+    Each memory item includes relevance scoring from search.
+    """
+
+    # Memory-specific fields
+    memory_subtype: MemorySubtype = field(default=MemorySubtype.EPISODIC)
+    memory_id: str | None = field(default=None)
+    relevance_score: float = field(default=0.0)
+    importance: float = field(default=0.5)
+    search_query: str = field(default="")
+
+    # Type-specific fields (populated based on memory_subtype)
+    key: str | None = field(default=None)  # For working memory
+    task_type: str | None = field(default=None)  # For episodic
+    outcome: str | None = field(default=None)  # For episodic
+    subject: str | None = field(default=None)  # For semantic
+    predicate: str | None = field(default=None)  # For semantic
+    object_value: str | None = field(default=None)  # For semantic
+    trigger: str | None = field(default=None)  # For procedural
+    success_rate: float | None = field(default=None)  # For procedural
+
+    def get_type(self) -> ContextType:
+        """Return MEMORY context type."""
+        return ContextType.MEMORY
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary with memory-specific fields."""
+        base = super().to_dict()
+        base.update(
+            {
+                "memory_subtype": self.memory_subtype.value,
+                "memory_id": self.memory_id,
+                "relevance_score": self.relevance_score,
+                "importance": self.importance,
+                "search_query": self.search_query,
+                "key": self.key,
+                "task_type": self.task_type,
+                "outcome": self.outcome,
+                "subject": self.subject,
+                "predicate": self.predicate,
+                "object_value": self.object_value,
+                "trigger": self.trigger,
+                "success_rate": self.success_rate,
+            }
+        )
+        return base
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "MemoryContext":
+        """Create MemoryContext from dictionary."""
+        return cls(
+            id=data.get("id", ""),
+            content=data["content"],
+            source=data["source"],
+            timestamp=datetime.fromisoformat(data["timestamp"])
+            if isinstance(data.get("timestamp"), str)
+            else data.get("timestamp", datetime.now(UTC)),
+            priority=data.get("priority", ContextPriority.NORMAL.value),
+            metadata=data.get("metadata", {}),
+            memory_subtype=MemorySubtype(data.get("memory_subtype", "episodic")),
+            memory_id=data.get("memory_id"),
+            relevance_score=data.get("relevance_score", 0.0),
+            importance=data.get("importance", 0.5),
+            search_query=data.get("search_query", ""),
+            key=data.get("key"),
+            task_type=data.get("task_type"),
+            outcome=data.get("outcome"),
+            subject=data.get("subject"),
+            predicate=data.get("predicate"),
+            object_value=data.get("object_value"),
+            trigger=data.get("trigger"),
+            success_rate=data.get("success_rate"),
+        )
+
+    @classmethod
+    def from_working_memory(
+        cls,
+        key: str,
+        value: Any,
+        source: str = "working_memory",
+        query: str = "",
+    ) -> "MemoryContext":
+        """
+        Create MemoryContext from working memory entry.
+
+        Args:
+            key: Working memory key
+            value: Value stored at key
+            source: Source identifier
+            query: Search query used
+
+        Returns:
+            MemoryContext instance
+        """
+        return cls(
+            content=str(value),
+            source=source,
+            memory_subtype=MemorySubtype.WORKING,
+            key=key,
+            relevance_score=1.0,  # Working memory is always relevant
+            importance=0.8,  # Higher importance for current session state
+            search_query=query,
+            priority=ContextPriority.HIGH.value,
+        )
+
+    @classmethod
+    def from_episodic_memory(
+        cls,
+        episode: Any,
+        query: str = "",
+    ) -> "MemoryContext":
+        """
+        Create MemoryContext from episodic memory episode.
+
+        Args:
+            episode: Episode object from episodic memory
+            query: Search query used
+
+        Returns:
+            MemoryContext instance
+        """
+        outcome_val = None
+        if hasattr(episode, "outcome") and episode.outcome:
+            outcome_val = (
+                episode.outcome.value
+                if hasattr(episode.outcome, "value")
+                else str(episode.outcome)
+            )
+
+        return cls(
+            content=episode.task_description,
+            source=f"episodic:{episode.id}",
+            memory_subtype=MemorySubtype.EPISODIC,
+            memory_id=str(episode.id),
+            relevance_score=getattr(episode, "importance_score", 0.5),
+            importance=getattr(episode, "importance_score", 0.5),
+            search_query=query,
+            task_type=getattr(episode, "task_type", None),
+            outcome=outcome_val,
+            metadata={
+                "session_id": getattr(episode, "session_id", None),
+                "occurred_at": episode.occurred_at.isoformat()
+                if hasattr(episode, "occurred_at") and episode.occurred_at
+                else None,
+                "lessons_learned": getattr(episode, "lessons_learned", []),
+            },
+        )
+
+    @classmethod
+    def from_semantic_memory(
+        cls,
+        fact: Any,
+        query: str = "",
+    ) -> "MemoryContext":
+        """
+        Create MemoryContext from semantic memory fact.
+
+        Args:
+            fact: Fact object from semantic memory
+            query: Search query used
+
+        Returns:
+            MemoryContext instance
+        """
+        triple = f"{fact.subject} {fact.predicate} {fact.object}"
+        return cls(
+            content=triple,
+            source=f"semantic:{fact.id}",
+            memory_subtype=MemorySubtype.SEMANTIC,
+            memory_id=str(fact.id),
+            relevance_score=getattr(fact, "confidence", 0.5),
+            importance=getattr(fact, "confidence", 0.5),
+            search_query=query,
+            subject=fact.subject,
+            predicate=fact.predicate,
+            object_value=fact.object,
+            priority=ContextPriority.NORMAL.value,
+        )
+
+    @classmethod
+    def from_procedural_memory(
+        cls,
+        procedure: Any,
+        query: str = "",
+    ) -> "MemoryContext":
+        """
+        Create MemoryContext from procedural memory procedure.
+
+        Args:
+            procedure: Procedure object from procedural memory
+            query: Search query used
+
+        Returns:
+            MemoryContext instance
+        """
+        # Format steps as content
+        steps = getattr(procedure, "steps", [])
+        steps_content = "\n".join(
+            f"  {i + 1}. {step.get('action', step) if isinstance(step, dict) else step}"
+            for i, step in enumerate(steps)
+        )
+        content = f"Procedure: {procedure.name}\nTrigger: {procedure.trigger_pattern}\nSteps:\n{steps_content}"
+
+        return cls(
+            content=content,
+            source=f"procedural:{procedure.id}",
+            memory_subtype=MemorySubtype.PROCEDURAL,
+            memory_id=str(procedure.id),
+            relevance_score=getattr(procedure, "success_rate", 0.5),
+            importance=0.7,  # Procedures are moderately important
+            search_query=query,
+            trigger=procedure.trigger_pattern,
+            success_rate=getattr(procedure, "success_rate", None),
+            metadata={
+                "steps_count": len(steps),
+                "execution_count": getattr(procedure, "success_count", 0)
+                + getattr(procedure, "failure_count", 0),
+            },
+        )
+
+    def is_working_memory(self) -> bool:
+        """Check if this is working memory."""
+        return self.memory_subtype == MemorySubtype.WORKING
+
+    def is_episodic_memory(self) -> bool:
+        """Check if this is episodic memory."""
+        return self.memory_subtype == MemorySubtype.EPISODIC
+
+    def is_semantic_memory(self) -> bool:
+        """Check if this is semantic memory."""
+        return self.memory_subtype == MemorySubtype.SEMANTIC
+
+    def is_procedural_memory(self) -> bool:
+        """Check if this is procedural memory."""
+        return self.memory_subtype == MemorySubtype.PROCEDURAL
+
+    def get_formatted_source(self) -> str:
+        """
+        Get a formatted source string for display.
+
+        Returns:
+            Formatted source string
+        """
+        parts = [f"[{self.memory_subtype.value}]", self.source]
+        if self.memory_id:
+            parts.append(f"({self.memory_id[:8]}...)")
+        return " ".join(parts)

backend/app/services/context/types/system.py

@@ -0,0 +1,138 @@
+"""
+System Context Type.
+
+Represents system prompts, instructions, and agent personas.
+"""
+
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from typing import Any
+
+from .base import BaseContext, ContextPriority, ContextType
+
+
+@dataclass(eq=False)
+class SystemContext(BaseContext):
+    """
+    Context for system prompts and instructions.
+
+    System context typically includes:
+    - Agent persona and role definitions
+    - Behavioral instructions
+    - Safety guidelines
+    - Output format requirements
+
+    System context is usually high priority and should
+    rarely be truncated or omitted.
+    """
+
+    # System context specific fields
+    role: str = field(default="assistant")
+    instructions_type: str = field(default="general")
+
+    def __post_init__(self) -> None:
+        """Set high priority for system context."""
+        # System context defaults to high priority
+        if self.priority == ContextPriority.NORMAL.value:
+            self.priority = ContextPriority.HIGH.value
+
+    def get_type(self) -> ContextType:
+        """Return SYSTEM context type."""
+        return ContextType.SYSTEM
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary with system-specific fields."""
+        base = super().to_dict()
+        base.update(
+            {
+                "role": self.role,
+                "instructions_type": self.instructions_type,
+            }
+        )
+        return base
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "SystemContext":
+        """Create SystemContext from dictionary."""
+        return cls(
+            id=data.get("id", ""),
+            content=data["content"],
+            source=data["source"],
+            timestamp=datetime.fromisoformat(data["timestamp"])
+            if isinstance(data.get("timestamp"), str)
+            else data.get("timestamp", datetime.now(UTC)),
+            priority=data.get("priority", ContextPriority.HIGH.value),
+            metadata=data.get("metadata", {}),
+            role=data.get("role", "assistant"),
+            instructions_type=data.get("instructions_type", "general"),
+        )
+
+    @classmethod
+    def create_persona(
+        cls,
+        name: str,
+        description: str,
+        capabilities: list[str] | None = None,
+        constraints: list[str] | None = None,
+    ) -> "SystemContext":
+        """
+        Create a persona system context.
+
+        Args:
+            name: Agent name/role
+            description: Role description
+            capabilities: List of things the agent can do
+            constraints: List of limitations
+
+        Returns:
+            SystemContext with formatted persona
+        """
+        parts = [f"You are {name}.", "", description]
+
+        if capabilities:
+            parts.append("")
+            parts.append("You can:")
+            for cap in capabilities:
+                parts.append(f"- {cap}")
+
+        if constraints:
+            parts.append("")
+            parts.append("You must not:")
+            for constraint in constraints:
+                parts.append(f"- {constraint}")
+
+        return cls(
+            content="\n".join(parts),
+            source="persona_builder",
+            role=name.lower().replace(" ", "_"),
+            instructions_type="persona",
+            priority=ContextPriority.HIGHEST.value,
+        )
+
+    @classmethod
+    def create_instructions(
+        cls,
+        instructions: str | list[str],
+        source: str = "instructions",
+    ) -> "SystemContext":
+        """
+        Create an instructions system context.
+
+        Args:
+            instructions: Instructions string or list of instruction strings
+            source: Source identifier
+
+        Returns:
+            SystemContext with instructions
+        """
+        if isinstance(instructions, list):
+            content = "\n".join(f"- {inst}" for inst in instructions)
+        else:
+            content = instructions
+
+        return cls(
+            content=content,
+            source=source,
+            instructions_type="instructions",
+            priority=ContextPriority.HIGH.value,
+        )

backend/app/services/context/types/task.py

@@ -0,0 +1,193 @@
+"""
+Task Context Type.
+
+Represents the current task or objective for the agent.
+"""
+
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from enum import Enum
+from typing import Any
+
+from .base import BaseContext, ContextPriority, ContextType
+
+
+class TaskStatus(str, Enum):
+    """Status of a task."""
+
+    PENDING = "pending"
+    IN_PROGRESS = "in_progress"
+    BLOCKED = "blocked"
+    COMPLETED = "completed"
+    FAILED = "failed"
+
+
+class TaskComplexity(str, Enum):
+    """Complexity level of a task."""
+
+    TRIVIAL = "trivial"
+    SIMPLE = "simple"
+    MODERATE = "moderate"
+    COMPLEX = "complex"
+    VERY_COMPLEX = "very_complex"
+
+
+@dataclass(eq=False)
+class TaskContext(BaseContext):
+    """
+    Context for the current task or objective.
+
+    Task context provides information about what the agent
+    should accomplish, including:
+    - Task description and goals
+    - Acceptance criteria
+    - Constraints and requirements
+    - Related issue/ticket information
+    """
+
+    # Task-specific fields
+    title: str = field(default="")
+    status: TaskStatus = field(default=TaskStatus.PENDING)
+    complexity: TaskComplexity = field(default=TaskComplexity.MODERATE)
+    issue_id: str | None = field(default=None)
+    project_id: str | None = field(default=None)
+    acceptance_criteria: list[str] = field(default_factory=list)
+    constraints: list[str] = field(default_factory=list)
+    parent_task_id: str | None = field(default=None)
+
+    # Note: TaskContext should typically have HIGH priority,
+    # but we don't auto-promote to allow explicit priority setting.
+    # Use TaskContext.create() for default HIGH priority behavior.
+
+    def get_type(self) -> ContextType:
+        """Return TASK context type."""
+        return ContextType.TASK
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary with task-specific fields."""
+        base = super().to_dict()
+        base.update(
+            {
+                "title": self.title,
+                "status": self.status.value,
+                "complexity": self.complexity.value,
+                "issue_id": self.issue_id,
+                "project_id": self.project_id,
+                "acceptance_criteria": self.acceptance_criteria,
+                "constraints": self.constraints,
+                "parent_task_id": self.parent_task_id,
+            }
+        )
+        return base
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "TaskContext":
+        """Create TaskContext from dictionary."""
+        status = data.get("status", "pending")
+        if isinstance(status, str):
+            status = TaskStatus(status)
+
+        complexity = data.get("complexity", "moderate")
+        if isinstance(complexity, str):
+            complexity = TaskComplexity(complexity)
+
+        return cls(
+            id=data.get("id", ""),
+            content=data["content"],
+            source=data.get("source", "task"),
+            timestamp=datetime.fromisoformat(data["timestamp"])
+            if isinstance(data.get("timestamp"), str)
+            else data.get("timestamp", datetime.now(UTC)),
+            priority=data.get("priority", ContextPriority.HIGH.value),
+            metadata=data.get("metadata", {}),
+            title=data.get("title", ""),
+            status=status,
+            complexity=complexity,
+            issue_id=data.get("issue_id"),
+            project_id=data.get("project_id"),
+            acceptance_criteria=data.get("acceptance_criteria", []),
+            constraints=data.get("constraints", []),
+            parent_task_id=data.get("parent_task_id"),
+        )
+
+    @classmethod
+    def create(
+        cls,
+        title: str,
+        description: str,
+        acceptance_criteria: list[str] | None = None,
+        constraints: list[str] | None = None,
+        issue_id: str | None = None,
+        project_id: str | None = None,
+        complexity: TaskComplexity | str = TaskComplexity.MODERATE,
+    ) -> "TaskContext":
+        """
+        Create a task context.
+
+        Args:
+            title: Task title
+            description: Task description
+            acceptance_criteria: List of acceptance criteria
+            constraints: List of constraints
+            issue_id: Related issue ID
+            project_id: Project ID
+            complexity: Task complexity
+
+        Returns:
+            TaskContext instance
+        """
+        if isinstance(complexity, str):
+            complexity = TaskComplexity(complexity)
+
+        return cls(
+            content=description,
+            source=f"task:{issue_id}" if issue_id else "task",
+            title=title,
+            status=TaskStatus.IN_PROGRESS,
+            complexity=complexity,
+            issue_id=issue_id,
+            project_id=project_id,
+            acceptance_criteria=acceptance_criteria or [],
+            constraints=constraints or [],
+        )
+
+    def format_for_prompt(self) -> str:
+        """
+        Format task for inclusion in prompt.
+
+        Returns:
+            Formatted task string
+        """
+        parts = []
+
+        if self.title:
+            parts.append(f"Task: {self.title}")
+            parts.append("")
+
+        parts.append(self.content)
+
+        if self.acceptance_criteria:
+            parts.append("")
+            parts.append("Acceptance Criteria:")
+            for criterion in self.acceptance_criteria:
+                parts.append(f"- {criterion}")
+
+        if self.constraints:
+            parts.append("")
+            parts.append("Constraints:")
+            for constraint in self.constraints:
+                parts.append(f"- {constraint}")
+
+        return "\n".join(parts)
+
+    def is_active(self) -> bool:
+        """Check if task is currently active."""
+        return self.status in (TaskStatus.PENDING, TaskStatus.IN_PROGRESS)
+
+    def is_complete(self) -> bool:
+        """Check if task is complete."""
+        return self.status == TaskStatus.COMPLETED
+
+    def is_blocked(self) -> bool:
+        """Check if task is blocked."""
+        return self.status == TaskStatus.BLOCKED

backend/app/services/context/types/tool.py

@@ -0,0 +1,211 @@
+"""
+Tool Context Type.
+
+Represents available tools and recent tool execution results.
+"""
+
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from enum import Enum
+from typing import Any
+
+from .base import BaseContext, ContextPriority, ContextType
+
+
+class ToolResultStatus(str, Enum):
+    """Status of a tool execution result."""
+
+    SUCCESS = "success"
+    ERROR = "error"
+    TIMEOUT = "timeout"
+    CANCELLED = "cancelled"
+
+
+@dataclass(eq=False)
+class ToolContext(BaseContext):
+    """
+    Context for tools and tool execution results.
+
+    Tool context includes:
+    - Tool descriptions and parameters
+    - Recent tool execution results
+    - Tool availability information
+
+    This helps the LLM understand what tools are available
+    and what results previous tool calls produced.
+    """
+
+    # Tool-specific fields
+    tool_name: str = field(default="")
+    tool_description: str = field(default="")
+    is_result: bool = field(default=False)
+    result_status: ToolResultStatus | None = field(default=None)
+    execution_time_ms: float | None = field(default=None)
+    parameters: dict[str, Any] = field(default_factory=dict)
+    server_name: str | None = field(default=None)
+
+    def get_type(self) -> ContextType:
+        """Return TOOL context type."""
+        return ContextType.TOOL
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary with tool-specific fields."""
+        base = super().to_dict()
+        base.update(
+            {
+                "tool_name": self.tool_name,
+                "tool_description": self.tool_description,
+                "is_result": self.is_result,
+                "result_status": self.result_status.value
+                if self.result_status
+                else None,
+                "execution_time_ms": self.execution_time_ms,
+                "parameters": self.parameters,
+                "server_name": self.server_name,
+            }
+        )
+        return base
+
+    @classmethod
+    def from_dict(cls, data: dict[str, Any]) -> "ToolContext":
+        """Create ToolContext from dictionary."""
+        result_status = data.get("result_status")
+        if isinstance(result_status, str):
+            result_status = ToolResultStatus(result_status)
+
+        return cls(
+            id=data.get("id", ""),
+            content=data["content"],
+            source=data.get("source", "tool"),
+            timestamp=datetime.fromisoformat(data["timestamp"])
+            if isinstance(data.get("timestamp"), str)
+            else data.get("timestamp", datetime.now(UTC)),
+            priority=data.get("priority", ContextPriority.NORMAL.value),
+            metadata=data.get("metadata", {}),
+            tool_name=data.get("tool_name", ""),
+            tool_description=data.get("tool_description", ""),
+            is_result=data.get("is_result", False),
+            result_status=result_status,
+            execution_time_ms=data.get("execution_time_ms"),
+            parameters=data.get("parameters", {}),
+            server_name=data.get("server_name"),
+        )
+
+    @classmethod
+    def from_tool_definition(
+        cls,
+        name: str,
+        description: str,
+        parameters: dict[str, Any] | None = None,
+        server_name: str | None = None,
+    ) -> "ToolContext":
+        """
+        Create a ToolContext from a tool definition.
+
+        Args:
+            name: Tool name
+            description: Tool description
+            parameters: Tool parameter schema
+            server_name: MCP server name
+
+        Returns:
+            ToolContext instance
+        """
+        # Format content as tool documentation
+        content_parts = [f"Tool: {name}", "", description]
+
+        if parameters:
+            content_parts.append("")
+            content_parts.append("Parameters:")
+            for param_name, param_info in parameters.items():
+                param_type = param_info.get("type", "any")
+                param_desc = param_info.get("description", "")
+                required = param_info.get("required", False)
+                req_marker = " (required)" if required else ""
+                content_parts.append(f"  - {param_name}: {param_type}{req_marker}")
+                if param_desc:
+                    content_parts.append(f"    {param_desc}")
+
+        return cls(
+            content="\n".join(content_parts),
+            source=f"tool:{server_name}:{name}" if server_name else f"tool:{name}",
+            tool_name=name,
+            tool_description=description,
+            is_result=False,
+            parameters=parameters or {},
+            server_name=server_name,
+            priority=ContextPriority.LOW.value,
+        )
+
+    @classmethod
+    def from_tool_result(
+        cls,
+        tool_name: str,
+        result: Any,
+        status: ToolResultStatus = ToolResultStatus.SUCCESS,
+        execution_time_ms: float | None = None,
+        parameters: dict[str, Any] | None = None,
+        server_name: str | None = None,
+    ) -> "ToolContext":
+        """
+        Create a ToolContext from a tool execution result.
+
+        Args:
+            tool_name: Name of the tool that was executed
+            result: Result content (will be converted to string)
+            status: Execution status
+            execution_time_ms: Execution time in milliseconds
+            parameters: Parameters that were passed to the tool
+            server_name: MCP server name
+
+        Returns:
+            ToolContext instance
+        """
+        # Convert result to string content
+        if isinstance(result, str):
+            content = result
+        elif isinstance(result, dict):
+            import json
+
+            try:
+                content = json.dumps(result, indent=2)
+            except (TypeError, ValueError):
+                content = str(result)
+        else:
+            content = str(result)
+
+        return cls(
+            content=content,
+            source=f"tool_result:{server_name}:{tool_name}"
+            if server_name
+            else f"tool_result:{tool_name}",
+            tool_name=tool_name,
+            is_result=True,
+            result_status=status,
+            execution_time_ms=execution_time_ms,
+            parameters=parameters or {},
+            server_name=server_name,
+            priority=ContextPriority.HIGH.value,  # Recent results are high priority
+        )
+
+    def is_successful(self) -> bool:
+        """Check if this is a successful tool result."""
+        return self.is_result and self.result_status == ToolResultStatus.SUCCESS
+
+    def is_error(self) -> bool:
+        """Check if this is an error result."""
+        return self.is_result and self.result_status == ToolResultStatus.ERROR
+
+    def format_for_prompt(self) -> str:
+        """
+        Format tool context for inclusion in prompt.
+
+        Returns:
+            Formatted tool string
+        """
+        if self.is_result:
+            status_str = self.result_status.value if self.result_status else "unknown"
+            header = f"Tool Result ({self.tool_name}, {status_str}):"
+            return f"{header}\n{self.content}"
+        else:
+            return self.content

backend/app/services/mcp/client_manager.py

@@ -122,16 +122,24 @@ class MCPClientManager:
             )
 
     async def _connect_all_servers(self) -> None:
-        """Connect to all enabled MCP servers."""
+        """Connect to all enabled MCP servers concurrently."""
+        import asyncio
+
         enabled_servers = self._registry.get_enabled_configs()
 
-        for name, config in enabled_servers.items():
+        async def connect_server(name: str, config: "MCPServerConfig") -> None:
             try:
                 await self._pool.get_connection(name, config)
                 logger.info("Connected to MCP server: %s", name)
             except Exception as e:
                 logger.error("Failed to connect to MCP server %s: %s", name, e)
 
+        # Connect to all servers concurrently for faster startup
+        await asyncio.gather(
+            *(connect_server(name, config) for name, config in enabled_servers.items()),
+            return_exceptions=True,
+        )
+
     async def shutdown(self) -> None:
         """
         Shutdown the MCP client manager.

backend/app/services/mcp/config.py

@@ -179,6 +179,8 @@ def load_mcp_config(path: str | Path | None = None) -> MCPConfig:
     2. MCP_CONFIG_PATH environment variable
     3. Default path (backend/mcp_servers.yaml)
     4. Empty config if no file exists
+
+    In test mode (IS_TEST=True), retry settings are reduced for faster tests.
     """
     if path is None:
         path = os.environ.get("MCP_CONFIG_PATH", str(DEFAULT_CONFIG_PATH))
@@ -189,7 +191,18 @@ def load_mcp_config(path: str | Path | None = None) -> MCPConfig:
         # Return empty config if no file exists (allows runtime registration)
         return MCPConfig()
 
-    return MCPConfig.from_yaml(path)
+    config = MCPConfig.from_yaml(path)
+
+    # In test mode, reduce retry settings to speed up tests
+    is_test = os.environ.get("IS_TEST", "").lower() in ("true", "1", "yes")
+    if is_test:
+        for server_config in config.mcp_servers.values():
+            server_config.retry_attempts = 1  # Single attempt
+            server_config.retry_delay = 0.1  # 100ms instead of 1s
+            server_config.retry_max_delay = 0.5  # 500ms max
+            server_config.timeout = 2  # 2s timeout instead of 30-120s
+
+    return config
 
 
 def create_default_config() -> MCPConfig:

backend/app/services/memory/__init__.py

@@ -0,0 +1,141 @@
+"""
+Agent Memory System
+
+Multi-tier cognitive memory for AI agents, providing:
+- Working Memory: Session-scoped ephemeral state (Redis/In-memory)
+- Episodic Memory: Experiential records of past tasks (PostgreSQL)
+- Semantic Memory: Learned facts and knowledge (PostgreSQL + pgvector)
+- Procedural Memory: Learned skills and procedures (PostgreSQL)
+
+Usage:
+    from app.services.memory import (
+        MemoryManager,
+        MemorySettings,
+        get_memory_settings,
+        MemoryType,
+        ScopeLevel,
+    )
+
+    # Create a manager for a session
+    manager = MemoryManager.for_session(
+        session_id="sess-123",
+        project_id=uuid,
+    )
+
+    async with manager:
+        # Working memory
+        await manager.set_working("key", {"data": "value"})
+        value = await manager.get_working("key")
+
+        # Episodic memory
+        episode = await manager.record_episode(episode_data)
+        similar = await manager.search_episodes("query")
+
+        # Semantic memory
+        fact = await manager.store_fact(fact_data)
+        facts = await manager.search_facts("query")
+
+        # Procedural memory
+        procedure = await manager.record_procedure(procedure_data)
+        procedures = await manager.find_procedures("context")
+"""
+
+# Configuration
+from .config import (
+    MemorySettings,
+    get_default_settings,
+    get_memory_settings,
+    reset_memory_settings,
+)
+
+# Exceptions
+from .exceptions import (
+    CheckpointError,
+    EmbeddingError,
+    MemoryCapacityError,
+    MemoryConflictError,
+    MemoryConsolidationError,
+    MemoryError,
+    MemoryExpiredError,
+    MemoryNotFoundError,
+    MemoryRetrievalError,
+    MemoryScopeError,
+    MemorySerializationError,
+    MemoryStorageError,
+)
+
+# Manager
+from .manager import MemoryManager
+
+# Types
+from .types import (
+    ConsolidationStatus,
+    ConsolidationType,
+    Episode,
+    EpisodeCreate,
+    Fact,
+    FactCreate,
+    MemoryItem,
+    MemoryStats,
+    MemoryStore,
+    MemoryType,
+    Outcome,
+    Procedure,
+    ProcedureCreate,
+    RetrievalResult,
+    ScopeContext,
+    ScopeLevel,
+    Step,
+    TaskState,
+    WorkingMemoryItem,
+)
+
+# Reflection (lazy import available)
+# Import directly: from app.services.memory.reflection import MemoryReflection
+
+__all__ = [
+    "CheckpointError",
+    "ConsolidationStatus",
+    "ConsolidationType",
+    "EmbeddingError",
+    "Episode",
+    "EpisodeCreate",
+    "Fact",
+    "FactCreate",
+    "MemoryCapacityError",
+    "MemoryConflictError",
+    "MemoryConsolidationError",
+    # Exceptions
+    "MemoryError",
+    "MemoryExpiredError",
+    "MemoryItem",
+    # Manager
+    "MemoryManager",
+    "MemoryNotFoundError",
+    "MemoryRetrievalError",
+    "MemoryScopeError",
+    "MemorySerializationError",
+    # Configuration
+    "MemorySettings",
+    "MemoryStats",
+    "MemoryStorageError",
+    # Types - Abstract
+    "MemoryStore",
+    # Types - Enums
+    "MemoryType",
+    "Outcome",
+    "Procedure",
+    "ProcedureCreate",
+    "RetrievalResult",
+    # Types - Data Classes
+    "ScopeContext",
+    "ScopeLevel",
+    "Step",
+    "TaskState",
+    "WorkingMemoryItem",
+    "get_default_settings",
+    "get_memory_settings",
+    "reset_memory_settings",
+    # MCP Tools - lazy import to avoid circular dependencies
+    # Import directly: from app.services.memory.mcp import MemoryToolService
+]

backend/app/services/memory/cache/__init__.py

@@ -0,0 +1,21 @@
+# app/services/memory/cache/__init__.py
+"""
+Memory Caching Layer.
+
+Provides caching for memory operations:
+- Hot Memory Cache: LRU cache for frequently accessed memories
+- Embedding Cache: Cache embeddings by content hash
+- Cache Manager: Unified cache management with invalidation
+"""
+
+from .cache_manager import CacheManager, CacheStats, get_cache_manager
+from .embedding_cache import EmbeddingCache
+from .hot_cache import HotMemoryCache
+
+__all__ = [
+    "CacheManager",
+    "CacheStats",
+    "EmbeddingCache",
+    "HotMemoryCache",
+    "get_cache_manager",
+]

backend/app/services/memory/cache/cache_manager.py

@@ -0,0 +1,505 @@
+# app/services/memory/cache/cache_manager.py
+"""
+Cache Manager.
+
+Unified cache management for memory operations.
+Coordinates hot cache, embedding cache, and retrieval cache.
+Provides centralized invalidation and statistics.
+"""
+
+import logging
+import threading
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from typing import TYPE_CHECKING, Any
+from uuid import UUID
+
+from app.services.memory.config import get_memory_settings
+
+from .embedding_cache import EmbeddingCache, create_embedding_cache
+from .hot_cache import CacheKey, HotMemoryCache, create_hot_cache
+
+if TYPE_CHECKING:
+    from redis.asyncio import Redis
+
+    from app.services.memory.indexing.retrieval import RetrievalCache
+
+logger = logging.getLogger(__name__)
+
+
+def _utcnow() -> datetime:
+    """Get current UTC time as timezone-aware datetime."""
+    return datetime.now(UTC)
+
+
+@dataclass
+class CacheStats:
+    """Aggregated cache statistics."""
+
+    hot_cache: dict[str, Any] = field(default_factory=dict)
+    embedding_cache: dict[str, Any] = field(default_factory=dict)
+    retrieval_cache: dict[str, Any] = field(default_factory=dict)
+    overall_hit_rate: float = 0.0
+    last_cleanup: datetime | None = None
+    cleanup_count: int = 0
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "hot_cache": self.hot_cache,
+            "embedding_cache": self.embedding_cache,
+            "retrieval_cache": self.retrieval_cache,
+            "overall_hit_rate": self.overall_hit_rate,
+            "last_cleanup": self.last_cleanup.isoformat()
+            if self.last_cleanup
+            else None,
+            "cleanup_count": self.cleanup_count,
+        }
+
+
+class CacheManager:
+    """
+    Unified cache manager for memory operations.
+
+    Provides:
+    - Centralized cache configuration
+    - Coordinated invalidation across caches
+    - Aggregated statistics
+    - Automatic cleanup scheduling
+
+    Performance targets:
+    - Overall cache hit rate > 80%
+    - Cache operations < 1ms (memory), < 5ms (Redis)
+    """
+
+    def __init__(
+        self,
+        hot_cache: HotMemoryCache[Any] | None = None,
+        embedding_cache: EmbeddingCache | None = None,
+        retrieval_cache: "RetrievalCache | None" = None,
+        redis: "Redis | None" = None,
+    ) -> None:
+        """
+        Initialize the cache manager.
+
+        Args:
+            hot_cache: Optional pre-configured hot cache
+            embedding_cache: Optional pre-configured embedding cache
+            retrieval_cache: Optional pre-configured retrieval cache
+            redis: Optional Redis connection for persistence
+        """
+        self._settings = get_memory_settings()
+        self._redis = redis
+        self._enabled = self._settings.cache_enabled
+
+        # Initialize caches
+        if hot_cache:
+            self._hot_cache = hot_cache
+        else:
+            self._hot_cache = create_hot_cache(
+                max_size=self._settings.cache_max_items,
+                default_ttl_seconds=self._settings.cache_ttl_seconds,
+            )
+
+        if embedding_cache:
+            self._embedding_cache = embedding_cache
+        else:
+            self._embedding_cache = create_embedding_cache(
+                max_size=self._settings.cache_max_items,
+                default_ttl_seconds=self._settings.cache_ttl_seconds
+                * 12,  # 1hr for embeddings
+                redis=redis,
+            )
+
+        self._retrieval_cache = retrieval_cache
+
+        # Stats tracking
+        self._last_cleanup: datetime | None = None
+        self._cleanup_count = 0
+        self._lock = threading.RLock()
+
+        logger.info(
+            f"Initialized CacheManager: enabled={self._enabled}, "
+            f"redis={'connected' if redis else 'disabled'}"
+        )
+
+    def set_redis(self, redis: "Redis") -> None:
+        """Set Redis connection for all caches."""
+        self._redis = redis
+        self._embedding_cache.set_redis(redis)
+
+    def set_retrieval_cache(self, cache: "RetrievalCache") -> None:
+        """Set retrieval cache instance."""
+        self._retrieval_cache = cache
+
+    @property
+    def is_enabled(self) -> bool:
+        """Check if caching is enabled."""
+        return self._enabled
+
+    @property
+    def hot_cache(self) -> HotMemoryCache[Any]:
+        """Get the hot memory cache."""
+        return self._hot_cache
+
+    @property
+    def embedding_cache(self) -> EmbeddingCache:
+        """Get the embedding cache."""
+        return self._embedding_cache
+
+    @property
+    def retrieval_cache(self) -> "RetrievalCache | None":
+        """Get the retrieval cache."""
+        return self._retrieval_cache
+
+    # =========================================================================
+    # Hot Memory Cache Operations
+    # =========================================================================
+
+    def get_memory(
+        self,
+        memory_type: str,
+        memory_id: UUID | str,
+        scope: str | None = None,
+    ) -> Any | None:
+        """
+        Get a memory from hot cache.
+
+        Args:
+            memory_type: Type of memory
+            memory_id: Memory ID
+            scope: Optional scope
+
+        Returns:
+            Cached memory or None
+        """
+        if not self._enabled:
+            return None
+        return self._hot_cache.get_by_id(memory_type, memory_id, scope)
+
+    def cache_memory(
+        self,
+        memory_type: str,
+        memory_id: UUID | str,
+        memory: Any,
+        scope: str | None = None,
+        ttl_seconds: float | None = None,
+    ) -> None:
+        """
+        Cache a memory in hot cache.
+
+        Args:
+            memory_type: Type of memory
+            memory_id: Memory ID
+            memory: Memory object
+            scope: Optional scope
+            ttl_seconds: Optional TTL override
+        """
+        if not self._enabled:
+            return
+        self._hot_cache.put_by_id(memory_type, memory_id, memory, scope, ttl_seconds)
+
+    # =========================================================================
+    # Embedding Cache Operations
+    # =========================================================================
+
+    async def get_embedding(
+        self,
+        content: str,
+        model: str = "default",
+    ) -> list[float] | None:
+        """
+        Get a cached embedding.
+
+        Args:
+            content: Content text
+            model: Model name
+
+        Returns:
+            Cached embedding or None
+        """
+        if not self._enabled:
+            return None
+        return await self._embedding_cache.get(content, model)
+
+    async def cache_embedding(
+        self,
+        content: str,
+        embedding: list[float],
+        model: str = "default",
+        ttl_seconds: float | None = None,
+    ) -> str:
+        """
+        Cache an embedding.
+
+        Args:
+            content: Content text
+            embedding: Embedding vector
+            model: Model name
+            ttl_seconds: Optional TTL override
+
+        Returns:
+            Content hash
+        """
+        if not self._enabled:
+            return EmbeddingCache.hash_content(content)
+        return await self._embedding_cache.put(content, embedding, model, ttl_seconds)
+
+    # =========================================================================
+    # Invalidation
+    # =========================================================================
+
+    async def invalidate_memory(
+        self,
+        memory_type: str,
+        memory_id: UUID | str,
+        scope: str | None = None,
+    ) -> int:
+        """
+        Invalidate a memory across all caches.
+
+        Args:
+            memory_type: Type of memory
+            memory_id: Memory ID
+            scope: Optional scope
+
+        Returns:
+            Number of entries invalidated
+        """
+        count = 0
+
+        # Invalidate hot cache
+        if self._hot_cache.invalidate_by_id(memory_type, memory_id, scope):
+            count += 1
+
+        # Invalidate retrieval cache
+        if self._retrieval_cache:
+            uuid_id = (
+                UUID(str(memory_id)) if not isinstance(memory_id, UUID) else memory_id
+            )
+            count += self._retrieval_cache.invalidate_by_memory(uuid_id)
+
+        logger.debug(f"Invalidated {count} cache entries for {memory_type}:{memory_id}")
+        return count
+
+    async def invalidate_by_type(self, memory_type: str) -> int:
+        """
+        Invalidate all entries of a memory type.
+
+        Args:
+            memory_type: Type of memory
+
+        Returns:
+            Number of entries invalidated
+        """
+        count = self._hot_cache.invalidate_by_type(memory_type)
+
+        if self._retrieval_cache:
+            count += self._retrieval_cache.clear()
+
+        logger.info(f"Invalidated {count} cache entries for type {memory_type}")
+        return count
+
+    async def invalidate_by_scope(self, scope: str) -> int:
+        """
+        Invalidate all entries in a scope.
+
+        Args:
+            scope: Scope to invalidate (e.g., project_id)
+
+        Returns:
+            Number of entries invalidated
+        """
+        count = self._hot_cache.invalidate_by_scope(scope)
+
+        # Retrieval cache doesn't support scope-based invalidation
+        # so we clear it entirely for safety
+        if self._retrieval_cache:
+            count += self._retrieval_cache.clear()
+
+        logger.info(f"Invalidated {count} cache entries for scope {scope}")
+        return count
+
+    async def invalidate_embedding(
+        self,
+        content: str,
+        model: str = "default",
+    ) -> bool:
+        """
+        Invalidate a cached embedding.
+
+        Args:
+            content: Content text
+            model: Model name
+
+        Returns:
+            True if entry was found and removed
+        """
+        return await self._embedding_cache.invalidate(content, model)
+
+    async def clear_all(self) -> int:
+        """
+        Clear all caches.
+
+        Returns:
+            Total number of entries cleared
+        """
+        count = 0
+
+        count += self._hot_cache.clear()
+        count += await self._embedding_cache.clear()
+
+        if self._retrieval_cache:
+            count += self._retrieval_cache.clear()
+
+        logger.info(f"Cleared {count} entries from all caches")
+        return count
+
+    # =========================================================================
+    # Cleanup
+    # =========================================================================
+
+    async def cleanup_expired(self) -> int:
+        """
+        Clean up expired entries from all caches.
+
+        Returns:
+            Number of entries cleaned up
+        """
+        with self._lock:
+            count = 0
+
+            count += self._hot_cache.cleanup_expired()
+            count += self._embedding_cache.cleanup_expired()
+
+            # Retrieval cache doesn't have a cleanup method,
+            # but entries expire on access
+
+            self._last_cleanup = _utcnow()
+            self._cleanup_count += 1
+
+            if count > 0:
+                logger.info(f"Cleaned up {count} expired cache entries")
+
+            return count
+
+    # =========================================================================
+    # Statistics
+    # =========================================================================
+
+    def get_stats(self) -> CacheStats:
+        """
+        Get aggregated cache statistics.
+
+        Returns:
+            CacheStats with all cache metrics
+        """
+        hot_stats = self._hot_cache.get_stats().to_dict()
+        emb_stats = self._embedding_cache.get_stats().to_dict()
+
+        retrieval_stats: dict[str, Any] = {}
+        if self._retrieval_cache:
+            retrieval_stats = self._retrieval_cache.get_stats()
+
+        # Calculate overall hit rate
+        total_hits = hot_stats.get("hits", 0) + emb_stats.get("hits", 0)
+        total_misses = hot_stats.get("misses", 0) + emb_stats.get("misses", 0)
+
+        if retrieval_stats:
+            # Retrieval cache doesn't track hits/misses the same way
+            pass
+
+        total_requests = total_hits + total_misses
+        overall_hit_rate = total_hits / total_requests if total_requests > 0 else 0.0
+
+        return CacheStats(
+            hot_cache=hot_stats,
+            embedding_cache=emb_stats,
+            retrieval_cache=retrieval_stats,
+            overall_hit_rate=overall_hit_rate,
+            last_cleanup=self._last_cleanup,
+            cleanup_count=self._cleanup_count,
+        )
+
+    def get_hot_memories(self, limit: int = 10) -> list[tuple[CacheKey, int]]:
+        """
+        Get the most frequently accessed memories.
+
+        Args:
+            limit: Maximum number to return
+
+        Returns:
+            List of (key, access_count) tuples
+        """
+        return self._hot_cache.get_hot_memories(limit)
+
+    def reset_stats(self) -> None:
+        """Reset all cache statistics."""
+        self._hot_cache.reset_stats()
+        self._embedding_cache.reset_stats()
+
+    # =========================================================================
+    # Warmup
+    # =========================================================================
+
+    async def warmup(
+        self,
+        memories: list[tuple[str, UUID | str, Any]],
+        scope: str | None = None,
+    ) -> int:
+        """
+        Warm up the hot cache with memories.
+
+        Args:
+            memories: List of (memory_type, memory_id, memory) tuples
+            scope: Optional scope for all memories
+
+        Returns:
+            Number of memories cached
+        """
+        if not self._enabled:
+            return 0
+
+        for memory_type, memory_id, memory in memories:
+            self._hot_cache.put_by_id(memory_type, memory_id, memory, scope)
+
+        logger.info(f"Warmed up cache with {len(memories)} memories")
+        return len(memories)
+
+
+# Singleton instance
+_cache_manager: CacheManager | None = None
+_cache_manager_lock = threading.Lock()
+
+
+def get_cache_manager(
+    redis: "Redis | None" = None,
+    reset: bool = False,
+) -> CacheManager:
+    """
+    Get the global CacheManager instance.
+
+    Thread-safe with double-checked locking pattern.
+
+    Args:
+        redis: Optional Redis connection
+        reset: Force create a new instance
+
+    Returns:
+        CacheManager instance
+    """
+    global _cache_manager
+
+    if reset or _cache_manager is None:
+        with _cache_manager_lock:
+            if reset or _cache_manager is None:
+                _cache_manager = CacheManager(redis=redis)
+
+    return _cache_manager
+
+
+def reset_cache_manager() -> None:
+    """Reset the global cache manager instance."""
+    global _cache_manager
+    with _cache_manager_lock:
+        _cache_manager = None

backend/app/services/memory/cache/embedding_cache.py

@@ -0,0 +1,623 @@
+# app/services/memory/cache/embedding_cache.py
+"""
+Embedding Cache.
+
+Caches embeddings by content hash to avoid recomputing.
+Provides significant performance improvement for repeated content.
+"""
+
+import hashlib
+import logging
+import threading
+from collections import OrderedDict
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from typing import TYPE_CHECKING, Any
+
+if TYPE_CHECKING:
+    from redis.asyncio import Redis
+
+logger = logging.getLogger(__name__)
+
+
+def _utcnow() -> datetime:
+    """Get current UTC time as timezone-aware datetime."""
+    return datetime.now(UTC)
+
+
+@dataclass
+class EmbeddingEntry:
+    """A cached embedding entry."""
+
+    embedding: list[float]
+    content_hash: str
+    model: str
+    created_at: datetime
+    ttl_seconds: float = 3600.0  # 1 hour default
+
+    def is_expired(self) -> bool:
+        """Check if this entry has expired."""
+        age = (_utcnow() - self.created_at).total_seconds()
+        return age > self.ttl_seconds
+
+
+@dataclass
+class EmbeddingCacheStats:
+    """Statistics for the embedding cache."""
+
+    hits: int = 0
+    misses: int = 0
+    evictions: int = 0
+    expirations: int = 0
+    current_size: int = 0
+    max_size: int = 0
+    bytes_saved: int = 0  # Estimated bytes saved by caching
+
+    @property
+    def hit_rate(self) -> float:
+        """Calculate cache hit rate."""
+        total = self.hits + self.misses
+        if total == 0:
+            return 0.0
+        return self.hits / total
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "hits": self.hits,
+            "misses": self.misses,
+            "evictions": self.evictions,
+            "expirations": self.expirations,
+            "current_size": self.current_size,
+            "max_size": self.max_size,
+            "hit_rate": self.hit_rate,
+            "bytes_saved": self.bytes_saved,
+        }
+
+
+class EmbeddingCache:
+    """
+    Cache for embeddings by content hash.
+
+    Features:
+    - Content-hash based deduplication
+    - LRU eviction
+    - TTL-based expiration
+    - Optional Redis backing for persistence
+    - Thread-safe operations
+
+    Performance targets:
+    - Cache hit rate > 90% for repeated content
+    - Get/put operations < 1ms (memory), < 5ms (Redis)
+    """
+
+    def __init__(
+        self,
+        max_size: int = 50000,
+        default_ttl_seconds: float = 3600.0,
+        redis: "Redis | None" = None,
+        redis_prefix: str = "mem:emb",
+    ) -> None:
+        """
+        Initialize the embedding cache.
+
+        Args:
+            max_size: Maximum number of entries in memory cache
+            default_ttl_seconds: Default TTL for entries (1 hour)
+            redis: Optional Redis connection for persistence
+            redis_prefix: Prefix for Redis keys
+        """
+        self._max_size = max_size
+        self._default_ttl = default_ttl_seconds
+        self._cache: OrderedDict[str, EmbeddingEntry] = OrderedDict()
+        self._lock = threading.RLock()
+        self._stats = EmbeddingCacheStats(max_size=max_size)
+        self._redis = redis
+        self._redis_prefix = redis_prefix
+
+        logger.info(
+            f"Initialized EmbeddingCache with max_size={max_size}, "
+            f"ttl={default_ttl_seconds}s, redis={'enabled' if redis else 'disabled'}"
+        )
+
+    def set_redis(self, redis: "Redis") -> None:
+        """Set Redis connection for persistence."""
+        self._redis = redis
+
+    @staticmethod
+    def hash_content(content: str) -> str:
+        """
+        Compute hash of content for cache key.
+
+        Args:
+            content: Content to hash
+
+        Returns:
+            32-character hex hash
+        """
+        return hashlib.sha256(content.encode()).hexdigest()[:32]
+
+    def _cache_key(self, content_hash: str, model: str) -> str:
+        """Build cache key from content hash and model."""
+        return f"{content_hash}:{model}"
+
+    def _redis_key(self, content_hash: str, model: str) -> str:
+        """Build Redis key from content hash and model."""
+        return f"{self._redis_prefix}:{content_hash}:{model}"
+
+    async def get(
+        self,
+        content: str,
+        model: str = "default",
+    ) -> list[float] | None:
+        """
+        Get a cached embedding.
+
+        Args:
+            content: Content text
+            model: Model name
+
+        Returns:
+            Cached embedding or None if not found/expired
+        """
+        content_hash = self.hash_content(content)
+        cache_key = self._cache_key(content_hash, model)
+
+        # Check memory cache first
+        with self._lock:
+            if cache_key in self._cache:
+                entry = self._cache[cache_key]
+                if entry.is_expired():
+                    del self._cache[cache_key]
+                    self._stats.expirations += 1
+                    self._stats.current_size = len(self._cache)
+                else:
+                    # Move to end (most recently used)
+                    self._cache.move_to_end(cache_key)
+                    self._stats.hits += 1
+                    return entry.embedding
+
+        # Check Redis if available
+        if self._redis:
+            try:
+                redis_key = self._redis_key(content_hash, model)
+                data = await self._redis.get(redis_key)
+                if data:
+                    import json
+
+                    embedding = json.loads(data)
+                    # Store in memory cache for faster access
+                    self._put_memory(content_hash, model, embedding)
+                    self._stats.hits += 1
+                    return embedding
+            except Exception as e:
+                logger.warning(f"Redis get error: {e}")
+
+        self._stats.misses += 1
+        return None
+
+    async def get_by_hash(
+        self,
+        content_hash: str,
+        model: str = "default",
+    ) -> list[float] | None:
+        """
+        Get a cached embedding by hash.
+
+        Args:
+            content_hash: Content hash
+            model: Model name
+
+        Returns:
+            Cached embedding or None if not found/expired
+        """
+        cache_key = self._cache_key(content_hash, model)
+
+        with self._lock:
+            if cache_key in self._cache:
+                entry = self._cache[cache_key]
+                if entry.is_expired():
+                    del self._cache[cache_key]
+                    self._stats.expirations += 1
+                    self._stats.current_size = len(self._cache)
+                else:
+                    self._cache.move_to_end(cache_key)
+                    self._stats.hits += 1
+                    return entry.embedding
+
+        # Check Redis
+        if self._redis:
+            try:
+                redis_key = self._redis_key(content_hash, model)
+                data = await self._redis.get(redis_key)
+                if data:
+                    import json
+
+                    embedding = json.loads(data)
+                    self._put_memory(content_hash, model, embedding)
+                    self._stats.hits += 1
+                    return embedding
+            except Exception as e:
+                logger.warning(f"Redis get error: {e}")
+
+        self._stats.misses += 1
+        return None
+
+    async def put(
+        self,
+        content: str,
+        embedding: list[float],
+        model: str = "default",
+        ttl_seconds: float | None = None,
+    ) -> str:
+        """
+        Cache an embedding.
+
+        Args:
+            content: Content text
+            embedding: Embedding vector
+            model: Model name
+            ttl_seconds: Optional TTL override
+
+        Returns:
+            Content hash
+        """
+        content_hash = self.hash_content(content)
+        ttl = ttl_seconds or self._default_ttl
+
+        # Store in memory
+        self._put_memory(content_hash, model, embedding, ttl)
+
+        # Store in Redis if available
+        if self._redis:
+            try:
+                import json
+
+                redis_key = self._redis_key(content_hash, model)
+                await self._redis.setex(
+                    redis_key,
+                    int(ttl),
+                    json.dumps(embedding),
+                )
+            except Exception as e:
+                logger.warning(f"Redis put error: {e}")
+
+        return content_hash
+
+    def _put_memory(
+        self,
+        content_hash: str,
+        model: str,
+        embedding: list[float],
+        ttl_seconds: float | None = None,
+    ) -> None:
+        """Store in memory cache."""
+        with self._lock:
+            # Evict if at capacity
+            self._evict_if_needed()
+
+            cache_key = self._cache_key(content_hash, model)
+            entry = EmbeddingEntry(
+                embedding=embedding,
+                content_hash=content_hash,
+                model=model,
+                created_at=_utcnow(),
+                ttl_seconds=ttl_seconds or self._default_ttl,
+            )
+
+            self._cache[cache_key] = entry
+            self._cache.move_to_end(cache_key)
+            self._stats.current_size = len(self._cache)
+
+    def _evict_if_needed(self) -> None:
+        """Evict entries if cache is at capacity."""
+        while len(self._cache) >= self._max_size:
+            if self._cache:
+                self._cache.popitem(last=False)
+                self._stats.evictions += 1
+
+    async def put_batch(
+        self,
+        items: list[tuple[str, list[float]]],
+        model: str = "default",
+        ttl_seconds: float | None = None,
+    ) -> list[str]:
+        """
+        Cache multiple embeddings.
+
+        Args:
+            items: List of (content, embedding) tuples
+            model: Model name
+            ttl_seconds: Optional TTL override
+
+        Returns:
+            List of content hashes
+        """
+        hashes = []
+        for content, embedding in items:
+            content_hash = await self.put(content, embedding, model, ttl_seconds)
+            hashes.append(content_hash)
+        return hashes
+
+    async def invalidate(
+        self,
+        content: str,
+        model: str = "default",
+    ) -> bool:
+        """
+        Invalidate a cached embedding.
+
+        Args:
+            content: Content text
+            model: Model name
+
+        Returns:
+            True if entry was found and removed
+        """
+        content_hash = self.hash_content(content)
+        return await self.invalidate_by_hash(content_hash, model)
+
+    async def invalidate_by_hash(
+        self,
+        content_hash: str,
+        model: str = "default",
+    ) -> bool:
+        """
+        Invalidate a cached embedding by hash.
+
+        Args:
+            content_hash: Content hash
+            model: Model name
+
+        Returns:
+            True if entry was found and removed
+        """
+        cache_key = self._cache_key(content_hash, model)
+        removed = False
+
+        with self._lock:
+            if cache_key in self._cache:
+                del self._cache[cache_key]
+                self._stats.current_size = len(self._cache)
+                removed = True
+
+        # Remove from Redis
+        if self._redis:
+            try:
+                redis_key = self._redis_key(content_hash, model)
+                await self._redis.delete(redis_key)
+                removed = True
+            except Exception as e:
+                logger.warning(f"Redis delete error: {e}")
+
+        return removed
+
+    async def invalidate_by_model(self, model: str) -> int:
+        """
+        Invalidate all embeddings for a model.
+
+        Args:
+            model: Model name
+
+        Returns:
+            Number of entries invalidated
+        """
+        count = 0
+
+        with self._lock:
+            keys_to_remove = [k for k, v in self._cache.items() if v.model == model]
+            for key in keys_to_remove:
+                del self._cache[key]
+                count += 1
+
+            self._stats.current_size = len(self._cache)
+
+        # Note: Redis pattern deletion would require SCAN which is expensive
+        # For now, we only clear memory cache for model-based invalidation
+
+        return count
+
+    async def clear(self) -> int:
+        """
+        Clear all cache entries.
+
+        Returns:
+            Number of entries cleared
+        """
+        with self._lock:
+            count = len(self._cache)
+            self._cache.clear()
+            self._stats.current_size = 0
+
+        # Clear Redis entries
+        if self._redis:
+            try:
+                pattern = f"{self._redis_prefix}:*"
+                deleted = 0
+                async for key in self._redis.scan_iter(match=pattern):
+                    await self._redis.delete(key)
+                    deleted += 1
+                count = max(count, deleted)
+            except Exception as e:
+                logger.warning(f"Redis clear error: {e}")
+
+        logger.info(f"Cleared {count} entries from embedding cache")
+        return count
+
+    def cleanup_expired(self) -> int:
+        """
+        Remove all expired entries from memory cache.
+
+        Returns:
+            Number of entries removed
+        """
+        with self._lock:
+            keys_to_remove = [k for k, v in self._cache.items() if v.is_expired()]
+            for key in keys_to_remove:
+                del self._cache[key]
+                self._stats.expirations += 1
+
+            self._stats.current_size = len(self._cache)
+
+            if keys_to_remove:
+                logger.debug(f"Cleaned up {len(keys_to_remove)} expired embeddings")
+
+            return len(keys_to_remove)
+
+    def get_stats(self) -> EmbeddingCacheStats:
+        """Get cache statistics."""
+        with self._lock:
+            self._stats.current_size = len(self._cache)
+            return self._stats
+
+    def reset_stats(self) -> None:
+        """Reset cache statistics."""
+        with self._lock:
+            self._stats = EmbeddingCacheStats(
+                max_size=self._max_size,
+                current_size=len(self._cache),
+            )
+
+    @property
+    def size(self) -> int:
+        """Get current cache size."""
+        return len(self._cache)
+
+    @property
+    def max_size(self) -> int:
+        """Get maximum cache size."""
+        return self._max_size
+
+
+class CachedEmbeddingGenerator:
+    """
+    Wrapper for embedding generators with caching.
+
+    Wraps an embedding generator to cache results.
+    """
+
+    def __init__(
+        self,
+        generator: Any,
+        cache: EmbeddingCache,
+        model: str = "default",
+    ) -> None:
+        """
+        Initialize the cached embedding generator.
+
+        Args:
+            generator: Underlying embedding generator
+            cache: Embedding cache
+            model: Model name for cache keys
+        """
+        self._generator = generator
+        self._cache = cache
+        self._model = model
+        self._call_count = 0
+        self._cache_hit_count = 0
+
+    async def generate(self, text: str) -> list[float]:
+        """
+        Generate embedding with caching.
+
+        Args:
+            text: Text to embed
+
+        Returns:
+            Embedding vector
+        """
+        self._call_count += 1
+
+        # Check cache first
+        cached = await self._cache.get(text, self._model)
+        if cached is not None:
+            self._cache_hit_count += 1
+            return cached
+
+        # Generate and cache
+        embedding = await self._generator.generate(text)
+        await self._cache.put(text, embedding, self._model)
+
+        return embedding
+
+    async def generate_batch(
+        self,
+        texts: list[str],
+    ) -> list[list[float]]:
+        """
+        Generate embeddings for multiple texts with caching.
+
+        Args:
+            texts: Texts to embed
+
+        Returns:
+            List of embedding vectors
+        """
+        results: list[list[float] | None] = [None] * len(texts)
+        to_generate: list[tuple[int, str]] = []
+
+        # Check cache for each text
+        for i, text in enumerate(texts):
+            cached = await self._cache.get(text, self._model)
+            if cached is not None:
+                results[i] = cached
+                self._cache_hit_count += 1
+            else:
+                to_generate.append((i, text))
+
+        self._call_count += len(texts)
+
+        # Generate missing embeddings
+        if to_generate:
+            if hasattr(self._generator, "generate_batch"):
+                texts_to_gen = [t for _, t in to_generate]
+                embeddings = await self._generator.generate_batch(texts_to_gen)
+
+                for (idx, text), embedding in zip(to_generate, embeddings, strict=True):
+                    results[idx] = embedding
+                    await self._cache.put(text, embedding, self._model)
+            else:
+                # Fallback to individual generation
+                for idx, text in to_generate:
+                    embedding = await self._generator.generate(text)
+                    results[idx] = embedding
+                    await self._cache.put(text, embedding, self._model)
+
+        return results  # type: ignore[return-value]
+
+    def get_stats(self) -> dict[str, Any]:
+        """Get generator statistics."""
+        return {
+            "call_count": self._call_count,
+            "cache_hit_count": self._cache_hit_count,
+            "cache_hit_rate": (
+                self._cache_hit_count / self._call_count
+                if self._call_count > 0
+                else 0.0
+            ),
+            "cache_stats": self._cache.get_stats().to_dict(),
+        }
+
+
+# Factory function
+def create_embedding_cache(
+    max_size: int = 50000,
+    default_ttl_seconds: float = 3600.0,
+    redis: "Redis | None" = None,
+) -> EmbeddingCache:
+    """
+    Create an embedding cache.
+
+    Args:
+        max_size: Maximum number of entries
+        default_ttl_seconds: Default TTL for entries
+        redis: Optional Redis connection
+
+    Returns:
+        Configured EmbeddingCache instance
+    """
+    return EmbeddingCache(
+        max_size=max_size,
+        default_ttl_seconds=default_ttl_seconds,
+        redis=redis,
+    )

backend/app/services/memory/cache/hot_cache.py

@@ -0,0 +1,461 @@
+# app/services/memory/cache/hot_cache.py
+"""
+Hot Memory Cache.
+
+LRU cache for frequently accessed memories.
+Provides fast access to recently used memories without database queries.
+"""
+
+import logging
+import threading
+from collections import OrderedDict
+from dataclasses import dataclass
+from datetime import UTC, datetime
+from typing import Any
+from uuid import UUID
+
+logger = logging.getLogger(__name__)
+
+
+def _utcnow() -> datetime:
+    """Get current UTC time as timezone-aware datetime."""
+    return datetime.now(UTC)
+
+
+@dataclass
+class CacheEntry[T]:
+    """A cached memory entry with metadata."""
+
+    value: T
+    created_at: datetime
+    last_accessed_at: datetime
+    access_count: int = 1
+    ttl_seconds: float = 300.0
+
+    def is_expired(self) -> bool:
+        """Check if this entry has expired."""
+        age = (_utcnow() - self.created_at).total_seconds()
+        return age > self.ttl_seconds
+
+    def touch(self) -> None:
+        """Update access time and count."""
+        self.last_accessed_at = _utcnow()
+        self.access_count += 1
+
+
+@dataclass
+class CacheKey:
+    """A structured cache key with components."""
+
+    memory_type: str
+    memory_id: str
+    scope: str | None = None
+
+    def __hash__(self) -> int:
+        return hash((self.memory_type, self.memory_id, self.scope))
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, CacheKey):
+            return False
+        return (
+            self.memory_type == other.memory_type
+            and self.memory_id == other.memory_id
+            and self.scope == other.scope
+        )
+
+    def __str__(self) -> str:
+        if self.scope:
+            return f"{self.memory_type}:{self.scope}:{self.memory_id}"
+        return f"{self.memory_type}:{self.memory_id}"
+
+
+@dataclass
+class HotCacheStats:
+    """Statistics for the hot memory cache."""
+
+    hits: int = 0
+    misses: int = 0
+    evictions: int = 0
+    expirations: int = 0
+    current_size: int = 0
+    max_size: int = 0
+
+    @property
+    def hit_rate(self) -> float:
+        """Calculate cache hit rate."""
+        total = self.hits + self.misses
+        if total == 0:
+            return 0.0
+        return self.hits / total
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "hits": self.hits,
+            "misses": self.misses,
+            "evictions": self.evictions,
+            "expirations": self.expirations,
+            "current_size": self.current_size,
+            "max_size": self.max_size,
+            "hit_rate": self.hit_rate,
+        }
+
+
+class HotMemoryCache[T]:
+    """
+    LRU cache for frequently accessed memories.
+
+    Features:
+    - LRU eviction when capacity is reached
+    - TTL-based expiration
+    - Access count tracking for hot memory identification
+    - Thread-safe operations
+    - Scoped invalidation
+
+    Performance targets:
+    - Cache hit rate > 80% for hot memories
+    - Get/put operations < 1ms
+    """
+
+    def __init__(
+        self,
+        max_size: int = 10000,
+        default_ttl_seconds: float = 300.0,
+    ) -> None:
+        """
+        Initialize the hot memory cache.
+
+        Args:
+            max_size: Maximum number of entries
+            default_ttl_seconds: Default TTL for entries (5 minutes)
+        """
+        self._max_size = max_size
+        self._default_ttl = default_ttl_seconds
+        self._cache: OrderedDict[CacheKey, CacheEntry[T]] = OrderedDict()
+        self._lock = threading.RLock()
+        self._stats = HotCacheStats(max_size=max_size)
+        logger.info(
+            f"Initialized HotMemoryCache with max_size={max_size}, "
+            f"ttl={default_ttl_seconds}s"
+        )
+
+    def get(self, key: CacheKey) -> T | None:
+        """
+        Get a memory from cache.
+
+        Args:
+            key: Cache key
+
+        Returns:
+            Cached value or None if not found/expired
+        """
+        with self._lock:
+            if key not in self._cache:
+                self._stats.misses += 1
+                return None
+
+            entry = self._cache[key]
+
+            # Check expiration
+            if entry.is_expired():
+                del self._cache[key]
+                self._stats.expirations += 1
+                self._stats.misses += 1
+                self._stats.current_size = len(self._cache)
+                return None
+
+            # Move to end (most recently used)
+            self._cache.move_to_end(key)
+            entry.touch()
+
+            self._stats.hits += 1
+            return entry.value
+
+    def get_by_id(
+        self,
+        memory_type: str,
+        memory_id: UUID | str,
+        scope: str | None = None,
+    ) -> T | None:
+        """
+        Get a memory by type and ID.
+
+        Args:
+            memory_type: Type of memory (episodic, semantic, procedural)
+            memory_id: Memory ID
+            scope: Optional scope (project_id, agent_id)
+
+        Returns:
+            Cached value or None if not found/expired
+        """
+        key = CacheKey(
+            memory_type=memory_type,
+            memory_id=str(memory_id),
+            scope=scope,
+        )
+        return self.get(key)
+
+    def put(
+        self,
+        key: CacheKey,
+        value: T,
+        ttl_seconds: float | None = None,
+    ) -> None:
+        """
+        Put a memory into cache.
+
+        Args:
+            key: Cache key
+            value: Value to cache
+            ttl_seconds: Optional TTL override
+        """
+        with self._lock:
+            # Evict if at capacity
+            self._evict_if_needed()
+
+            now = _utcnow()
+            entry = CacheEntry(
+                value=value,
+                created_at=now,
+                last_accessed_at=now,
+                access_count=1,
+                ttl_seconds=ttl_seconds or self._default_ttl,
+            )
+
+            self._cache[key] = entry
+            self._cache.move_to_end(key)
+            self._stats.current_size = len(self._cache)
+
+    def put_by_id(
+        self,
+        memory_type: str,
+        memory_id: UUID | str,
+        value: T,
+        scope: str | None = None,
+        ttl_seconds: float | None = None,
+    ) -> None:
+        """
+        Put a memory by type and ID.
+
+        Args:
+            memory_type: Type of memory
+            memory_id: Memory ID
+            value: Value to cache
+            scope: Optional scope
+            ttl_seconds: Optional TTL override
+        """
+        key = CacheKey(
+            memory_type=memory_type,
+            memory_id=str(memory_id),
+            scope=scope,
+        )
+        self.put(key, value, ttl_seconds)
+
+    def _evict_if_needed(self) -> None:
+        """Evict entries if cache is at capacity."""
+        while len(self._cache) >= self._max_size:
+            # Remove least recently used (first item)
+            if self._cache:
+                self._cache.popitem(last=False)
+                self._stats.evictions += 1
+
+    def invalidate(self, key: CacheKey) -> bool:
+        """
+        Invalidate a specific cache entry.
+
+        Args:
+            key: Cache key to invalidate
+
+        Returns:
+            True if entry was found and removed
+        """
+        with self._lock:
+            if key in self._cache:
+                del self._cache[key]
+                self._stats.current_size = len(self._cache)
+                return True
+            return False
+
+    def invalidate_by_id(
+        self,
+        memory_type: str,
+        memory_id: UUID | str,
+        scope: str | None = None,
+    ) -> bool:
+        """
+        Invalidate a memory by type and ID.
+
+        Args:
+            memory_type: Type of memory
+            memory_id: Memory ID
+            scope: Optional scope
+
+        Returns:
+            True if entry was found and removed
+        """
+        key = CacheKey(
+            memory_type=memory_type,
+            memory_id=str(memory_id),
+            scope=scope,
+        )
+        return self.invalidate(key)
+
+    def invalidate_by_type(self, memory_type: str) -> int:
+        """
+        Invalidate all entries of a memory type.
+
+        Args:
+            memory_type: Type of memory to invalidate
+
+        Returns:
+            Number of entries invalidated
+        """
+        with self._lock:
+            keys_to_remove = [
+                k for k in self._cache.keys() if k.memory_type == memory_type
+            ]
+            for key in keys_to_remove:
+                del self._cache[key]
+
+            self._stats.current_size = len(self._cache)
+            return len(keys_to_remove)
+
+    def invalidate_by_scope(self, scope: str) -> int:
+        """
+        Invalidate all entries in a scope.
+
+        Args:
+            scope: Scope to invalidate (e.g., project_id)
+
+        Returns:
+            Number of entries invalidated
+        """
+        with self._lock:
+            keys_to_remove = [k for k in self._cache.keys() if k.scope == scope]
+            for key in keys_to_remove:
+                del self._cache[key]
+
+            self._stats.current_size = len(self._cache)
+            return len(keys_to_remove)
+
+    def invalidate_pattern(self, pattern: str) -> int:
+        """
+        Invalidate entries matching a pattern.
+
+        Pattern can include * as wildcard.
+
+        Args:
+            pattern: Pattern to match (e.g., "episodic:*")
+
+        Returns:
+            Number of entries invalidated
+        """
+        import fnmatch
+
+        with self._lock:
+            keys_to_remove = [
+                k for k in self._cache.keys() if fnmatch.fnmatch(str(k), pattern)
+            ]
+            for key in keys_to_remove:
+                del self._cache[key]
+
+            self._stats.current_size = len(self._cache)
+            return len(keys_to_remove)
+
+    def clear(self) -> int:
+        """
+        Clear all cache entries.
+
+        Returns:
+            Number of entries cleared
+        """
+        with self._lock:
+            count = len(self._cache)
+            self._cache.clear()
+            self._stats.current_size = 0
+            logger.info(f"Cleared {count} entries from hot cache")
+            return count
+
+    def cleanup_expired(self) -> int:
+        """
+        Remove all expired entries.
+
+        Returns:
+            Number of entries removed
+        """
+        with self._lock:
+            keys_to_remove = [k for k, v in self._cache.items() if v.is_expired()]
+            for key in keys_to_remove:
+                del self._cache[key]
+                self._stats.expirations += 1
+
+            self._stats.current_size = len(self._cache)
+
+            if keys_to_remove:
+                logger.debug(f"Cleaned up {len(keys_to_remove)} expired entries")
+
+            return len(keys_to_remove)
+
+    def get_hot_memories(self, limit: int = 10) -> list[tuple[CacheKey, int]]:
+        """
+        Get the most frequently accessed memories.
+
+        Args:
+            limit: Maximum number of memories to return
+
+        Returns:
+            List of (key, access_count) tuples sorted by access count
+        """
+        with self._lock:
+            entries = [
+                (k, v.access_count)
+                for k, v in self._cache.items()
+                if not v.is_expired()
+            ]
+            entries.sort(key=lambda x: x[1], reverse=True)
+            return entries[:limit]
+
+    def get_stats(self) -> HotCacheStats:
+        """Get cache statistics."""
+        with self._lock:
+            self._stats.current_size = len(self._cache)
+            return self._stats
+
+    def reset_stats(self) -> None:
+        """Reset cache statistics."""
+        with self._lock:
+            self._stats = HotCacheStats(
+                max_size=self._max_size,
+                current_size=len(self._cache),
+            )
+
+    @property
+    def size(self) -> int:
+        """Get current cache size."""
+        return len(self._cache)
+
+    @property
+    def max_size(self) -> int:
+        """Get maximum cache size."""
+        return self._max_size
+
+
+# Factory function for typed caches
+def create_hot_cache(
+    max_size: int = 10000,
+    default_ttl_seconds: float = 300.0,
+) -> HotMemoryCache[Any]:
+    """
+    Create a hot memory cache.
+
+    Args:
+        max_size: Maximum number of entries
+        default_ttl_seconds: Default TTL for entries
+
+    Returns:
+        Configured HotMemoryCache instance
+    """
+    return HotMemoryCache(
+        max_size=max_size,
+        default_ttl_seconds=default_ttl_seconds,
+    )

backend/app/services/memory/config.py

@@ -0,0 +1,410 @@
+"""
+Memory System Configuration.
+
+Provides Pydantic settings for the Agent Memory System,
+including storage backends, capacity limits, and consolidation policies.
+"""
+
+import threading
+from functools import lru_cache
+from typing import Any
+
+from pydantic import Field, field_validator, model_validator
+from pydantic_settings import BaseSettings
+
+
+class MemorySettings(BaseSettings):
+    """
+    Configuration for the Agent Memory System.
+
+    All settings can be overridden via environment variables
+    with the MEM_ prefix.
+    """
+
+    # Working Memory Settings
+    working_memory_backend: str = Field(
+        default="redis",
+        description="Backend for working memory: 'redis' or 'memory'",
+    )
+    working_memory_default_ttl_seconds: int = Field(
+        default=3600,
+        ge=60,
+        le=86400,
+        description="Default TTL for working memory items (1 hour default)",
+    )
+    working_memory_max_items_per_session: int = Field(
+        default=1000,
+        ge=100,
+        le=100000,
+        description="Maximum items per session in working memory",
+    )
+    working_memory_max_value_size_bytes: int = Field(
+        default=1048576,  # 1MB
+        ge=1024,
+        le=104857600,  # 100MB
+        description="Maximum size of a single value in working memory",
+    )
+    working_memory_checkpoint_enabled: bool = Field(
+        default=True,
+        description="Enable checkpointing for working memory recovery",
+    )
+
+    # Redis Settings (for working memory)
+    redis_url: str = Field(
+        default="redis://localhost:6379/0",
+        description="Redis connection URL",
+    )
+    redis_prefix: str = Field(
+        default="mem",
+        description="Redis key prefix for memory items",
+    )
+    redis_connection_timeout_seconds: int = Field(
+        default=5,
+        ge=1,
+        le=60,
+        description="Redis connection timeout",
+    )
+
+    # Episodic Memory Settings
+    episodic_max_episodes_per_project: int = Field(
+        default=10000,
+        ge=100,
+        le=1000000,
+        description="Maximum episodes to retain per project",
+    )
+    episodic_default_importance: float = Field(
+        default=0.5,
+        ge=0.0,
+        le=1.0,
+        description="Default importance score for new episodes",
+    )
+    episodic_retention_days: int = Field(
+        default=365,
+        ge=7,
+        le=3650,
+        description="Days to retain episodes before archival",
+    )
+
+    # Semantic Memory Settings
+    semantic_max_facts_per_project: int = Field(
+        default=50000,
+        ge=1000,
+        le=10000000,
+        description="Maximum facts to retain per project",
+    )
+    semantic_confidence_decay_days: int = Field(
+        default=90,
+        ge=7,
+        le=365,
+        description="Days until confidence decays by 50%",
+    )
+    semantic_min_confidence: float = Field(
+        default=0.1,
+        ge=0.0,
+        le=1.0,
+        description="Minimum confidence before fact is pruned",
+    )
+
+    # Procedural Memory Settings
+    procedural_max_procedures_per_project: int = Field(
+        default=1000,
+        ge=10,
+        le=100000,
+        description="Maximum procedures per project",
+    )
+    procedural_min_success_rate: float = Field(
+        default=0.3,
+        ge=0.0,
+        le=1.0,
+        description="Minimum success rate before procedure is pruned",
+    )
+    procedural_min_uses_before_suggest: int = Field(
+        default=3,
+        ge=1,
+        le=100,
+        description="Minimum uses before procedure is suggested",
+    )
+
+    # Embedding Settings
+    embedding_model: str = Field(
+        default="text-embedding-3-small",
+        description="Model to use for embeddings",
+    )
+    embedding_dimensions: int = Field(
+        default=1536,
+        ge=256,
+        le=4096,
+        description="Embedding vector dimensions",
+    )
+    embedding_batch_size: int = Field(
+        default=100,
+        ge=1,
+        le=1000,
+        description="Batch size for embedding generation",
+    )
+    embedding_cache_enabled: bool = Field(
+        default=True,
+        description="Enable caching of embeddings",
+    )
+
+    # Retrieval Settings
+    retrieval_default_limit: int = Field(
+        default=10,
+        ge=1,
+        le=100,
+        description="Default limit for retrieval queries",
+    )
+    retrieval_max_limit: int = Field(
+        default=100,
+        ge=10,
+        le=1000,
+        description="Maximum limit for retrieval queries",
+    )
+    retrieval_min_similarity: float = Field(
+        default=0.5,
+        ge=0.0,
+        le=1.0,
+        description="Minimum similarity score for retrieval",
+    )
+
+    # Consolidation Settings
+    consolidation_enabled: bool = Field(
+        default=True,
+        description="Enable automatic memory consolidation",
+    )
+    consolidation_batch_size: int = Field(
+        default=100,
+        ge=10,
+        le=1000,
+        description="Batch size for consolidation jobs",
+    )
+    consolidation_schedule_cron: str = Field(
+        default="0 3 * * *",
+        description="Cron expression for nightly consolidation (3 AM)",
+    )
+    consolidation_working_to_episodic_delay_minutes: int = Field(
+        default=30,
+        ge=5,
+        le=1440,
+        description="Minutes after session end before consolidating to episodic",
+    )
+
+    # Pruning Settings
+    pruning_enabled: bool = Field(
+        default=True,
+        description="Enable automatic memory pruning",
+    )
+    pruning_min_age_days: int = Field(
+        default=7,
+        ge=1,
+        le=365,
+        description="Minimum age before memory can be pruned",
+    )
+    pruning_importance_threshold: float = Field(
+        default=0.2,
+        ge=0.0,
+        le=1.0,
+        description="Importance threshold below which memory can be pruned",
+    )
+
+    # Caching Settings
+    cache_enabled: bool = Field(
+        default=True,
+        description="Enable caching for memory retrieval",
+    )
+    cache_ttl_seconds: int = Field(
+        default=300,
+        ge=10,
+        le=3600,
+        description="Cache TTL for retrieval results",
+    )
+    cache_max_items: int = Field(
+        default=10000,
+        ge=100,
+        le=1000000,
+        description="Maximum items in memory cache",
+    )
+
+    # Performance Settings
+    max_retrieval_time_ms: int = Field(
+        default=100,
+        ge=10,
+        le=5000,
+        description="Target maximum retrieval time in milliseconds",
+    )
+    parallel_retrieval: bool = Field(
+        default=True,
+        description="Enable parallel retrieval from multiple memory types",
+    )
+    max_parallel_retrievals: int = Field(
+        default=4,
+        ge=1,
+        le=10,
+        description="Maximum concurrent retrieval operations",
+    )
+
+    @field_validator("working_memory_backend")
+    @classmethod
+    def validate_backend(cls, v: str) -> str:
+        """Validate working memory backend."""
+        valid_backends = {"redis", "memory"}
+        if v not in valid_backends:
+            raise ValueError(f"backend must be one of: {valid_backends}")
+        return v
+
+    @field_validator("embedding_model")
+    @classmethod
+    def validate_embedding_model(cls, v: str) -> str:
+        """Validate embedding model name."""
+        valid_models = {
+            "text-embedding-3-small",
+            "text-embedding-3-large",
+            "text-embedding-ada-002",
+        }
+        if v not in valid_models:
+            raise ValueError(f"embedding_model must be one of: {valid_models}")
+        return v
+
+    @model_validator(mode="after")
+    def validate_limits(self) -> "MemorySettings":
+        """Validate that limits are consistent."""
+        if self.retrieval_default_limit > self.retrieval_max_limit:
+            raise ValueError(
+                f"retrieval_default_limit ({self.retrieval_default_limit}) "
+                f"cannot exceed retrieval_max_limit ({self.retrieval_max_limit})"
+            )
+        return self
+
+    def get_working_memory_config(self) -> dict[str, Any]:
+        """Get working memory configuration as a dictionary."""
+        return {
+            "backend": self.working_memory_backend,
+            "default_ttl_seconds": self.working_memory_default_ttl_seconds,
+            "max_items_per_session": self.working_memory_max_items_per_session,
+            "max_value_size_bytes": self.working_memory_max_value_size_bytes,
+            "checkpoint_enabled": self.working_memory_checkpoint_enabled,
+        }
+
+    def get_redis_config(self) -> dict[str, Any]:
+        """Get Redis configuration as a dictionary."""
+        return {
+            "url": self.redis_url,
+            "prefix": self.redis_prefix,
+            "connection_timeout_seconds": self.redis_connection_timeout_seconds,
+        }
+
+    def get_embedding_config(self) -> dict[str, Any]:
+        """Get embedding configuration as a dictionary."""
+        return {
+            "model": self.embedding_model,
+            "dimensions": self.embedding_dimensions,
+            "batch_size": self.embedding_batch_size,
+            "cache_enabled": self.embedding_cache_enabled,
+        }
+
+    def get_consolidation_config(self) -> dict[str, Any]:
+        """Get consolidation configuration as a dictionary."""
+        return {
+            "enabled": self.consolidation_enabled,
+            "batch_size": self.consolidation_batch_size,
+            "schedule_cron": self.consolidation_schedule_cron,
+            "working_to_episodic_delay_minutes": (
+                self.consolidation_working_to_episodic_delay_minutes
+            ),
+        }
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert settings to dictionary for logging/debugging."""
+        return {
+            "working_memory": self.get_working_memory_config(),
+            "redis": self.get_redis_config(),
+            "episodic": {
+                "max_episodes_per_project": self.episodic_max_episodes_per_project,
+                "default_importance": self.episodic_default_importance,
+                "retention_days": self.episodic_retention_days,
+            },
+            "semantic": {
+                "max_facts_per_project": self.semantic_max_facts_per_project,
+                "confidence_decay_days": self.semantic_confidence_decay_days,
+                "min_confidence": self.semantic_min_confidence,
+            },
+            "procedural": {
+                "max_procedures_per_project": self.procedural_max_procedures_per_project,
+                "min_success_rate": self.procedural_min_success_rate,
+                "min_uses_before_suggest": self.procedural_min_uses_before_suggest,
+            },
+            "embedding": self.get_embedding_config(),
+            "retrieval": {
+                "default_limit": self.retrieval_default_limit,
+                "max_limit": self.retrieval_max_limit,
+                "min_similarity": self.retrieval_min_similarity,
+            },
+            "consolidation": self.get_consolidation_config(),
+            "pruning": {
+                "enabled": self.pruning_enabled,
+                "min_age_days": self.pruning_min_age_days,
+                "importance_threshold": self.pruning_importance_threshold,
+            },
+            "cache": {
+                "enabled": self.cache_enabled,
+                "ttl_seconds": self.cache_ttl_seconds,
+                "max_items": self.cache_max_items,
+            },
+            "performance": {
+                "max_retrieval_time_ms": self.max_retrieval_time_ms,
+                "parallel_retrieval": self.parallel_retrieval,
+                "max_parallel_retrievals": self.max_parallel_retrievals,
+            },
+        }
+
+    model_config = {
+        "env_prefix": "MEM_",
+        "env_file": ".env",
+        "env_file_encoding": "utf-8",
+        "case_sensitive": False,
+        "extra": "ignore",
+    }
+
+
+# Thread-safe singleton pattern
+_settings: MemorySettings | None = None
+_settings_lock = threading.Lock()
+
+
+def get_memory_settings() -> MemorySettings:
+    """
+    Get the global MemorySettings instance.
+
+    Thread-safe with double-checked locking pattern.
+
+    Returns:
+        MemorySettings instance
+    """
+    global _settings
+    if _settings is None:
+        with _settings_lock:
+            if _settings is None:
+                _settings = MemorySettings()
+    return _settings
+
+
+def reset_memory_settings() -> None:
+    """
+    Reset the global settings instance.
+
+    Primarily used for testing.
+    """
+    global _settings
+    with _settings_lock:
+        _settings = None
+
+
+@lru_cache(maxsize=1)
+def get_default_settings() -> MemorySettings:
+    """
+    Get default settings (cached).
+
+    Use this for read-only access to defaults.
+    For mutable access, use get_memory_settings().
+    """
+    return MemorySettings()

backend/app/services/memory/consolidation/__init__.py

@@ -0,0 +1,29 @@
+# app/services/memory/consolidation/__init__.py
+"""
+Memory Consolidation.
+
+Transfers and extracts knowledge between memory tiers:
+- Working -> Episodic (session end)
+- Episodic -> Semantic (learn facts)
+- Episodic -> Procedural (learn procedures)
+
+Also handles memory pruning and importance-based retention.
+"""
+
+from .service import (
+    ConsolidationConfig,
+    ConsolidationResult,
+    MemoryConsolidationService,
+    NightlyConsolidationResult,
+    SessionConsolidationResult,
+    get_consolidation_service,
+)
+
+__all__ = [
+    "ConsolidationConfig",
+    "ConsolidationResult",
+    "MemoryConsolidationService",
+    "NightlyConsolidationResult",
+    "SessionConsolidationResult",
+    "get_consolidation_service",
+]

backend/app/services/memory/consolidation/service.py

@@ -0,0 +1,913 @@
+# app/services/memory/consolidation/service.py
+"""
+Memory Consolidation Service.
+
+Transfers and extracts knowledge between memory tiers:
+- Working -> Episodic (session end)
+- Episodic -> Semantic (learn facts)
+- Episodic -> Procedural (learn procedures)
+
+Also handles memory pruning and importance-based retention.
+"""
+
+import logging
+from dataclasses import dataclass, field
+from datetime import UTC, datetime, timedelta
+from typing import Any
+from uuid import UUID
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.services.memory.episodic.memory import EpisodicMemory
+from app.services.memory.procedural.memory import ProceduralMemory
+from app.services.memory.semantic.extraction import FactExtractor, get_fact_extractor
+from app.services.memory.semantic.memory import SemanticMemory
+from app.services.memory.types import (
+    Episode,
+    EpisodeCreate,
+    Outcome,
+    ProcedureCreate,
+    TaskState,
+)
+from app.services.memory.working.memory import WorkingMemory
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ConsolidationConfig:
+    """Configuration for memory consolidation."""
+
+    # Working -> Episodic thresholds
+    min_steps_for_episode: int = 2
+    min_duration_seconds: float = 5.0
+
+    # Episodic -> Semantic thresholds
+    min_confidence_for_fact: float = 0.6
+    max_facts_per_episode: int = 10
+    reinforce_existing_facts: bool = True
+
+    # Episodic -> Procedural thresholds
+    min_episodes_for_procedure: int = 3
+    min_success_rate_for_procedure: float = 0.7
+    min_steps_for_procedure: int = 2
+
+    # Pruning thresholds
+    max_episode_age_days: int = 90
+    min_importance_to_keep: float = 0.2
+    keep_all_failures: bool = True
+    keep_all_with_lessons: bool = True
+
+    # Batch sizes
+    batch_size: int = 100
+
+
+@dataclass
+class ConsolidationResult:
+    """Result of a consolidation operation."""
+
+    source_type: str
+    target_type: str
+    items_processed: int = 0
+    items_created: int = 0
+    items_updated: int = 0
+    items_skipped: int = 0
+    items_pruned: int = 0
+    errors: list[str] = field(default_factory=list)
+    duration_seconds: float = 0.0
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "source_type": self.source_type,
+            "target_type": self.target_type,
+            "items_processed": self.items_processed,
+            "items_created": self.items_created,
+            "items_updated": self.items_updated,
+            "items_skipped": self.items_skipped,
+            "items_pruned": self.items_pruned,
+            "errors": self.errors,
+            "duration_seconds": self.duration_seconds,
+        }
+
+
+@dataclass
+class SessionConsolidationResult:
+    """Result of consolidating a session's working memory to episodic."""
+
+    session_id: str
+    episode_created: bool = False
+    episode_id: UUID | None = None
+    scratchpad_entries: int = 0
+    variables_captured: int = 0
+    error: str | None = None
+
+
+@dataclass
+class NightlyConsolidationResult:
+    """Result of nightly consolidation run."""
+
+    started_at: datetime
+    completed_at: datetime | None = None
+    episodic_to_semantic: ConsolidationResult | None = None
+    episodic_to_procedural: ConsolidationResult | None = None
+    pruning: ConsolidationResult | None = None
+    total_episodes_processed: int = 0
+    total_facts_created: int = 0
+    total_procedures_created: int = 0
+    total_pruned: int = 0
+    errors: list[str] = field(default_factory=list)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "started_at": self.started_at.isoformat(),
+            "completed_at": self.completed_at.isoformat()
+            if self.completed_at
+            else None,
+            "episodic_to_semantic": (
+                self.episodic_to_semantic.to_dict()
+                if self.episodic_to_semantic
+                else None
+            ),
+            "episodic_to_procedural": (
+                self.episodic_to_procedural.to_dict()
+                if self.episodic_to_procedural
+                else None
+            ),
+            "pruning": self.pruning.to_dict() if self.pruning else None,
+            "total_episodes_processed": self.total_episodes_processed,
+            "total_facts_created": self.total_facts_created,
+            "total_procedures_created": self.total_procedures_created,
+            "total_pruned": self.total_pruned,
+            "errors": self.errors,
+        }
+
+
+class MemoryConsolidationService:
+    """
+    Service for consolidating memories between tiers.
+
+    Responsibilities:
+    - Transfer working memory to episodic at session end
+    - Extract facts from episodes to semantic memory
+    - Learn procedures from successful episode patterns
+    - Prune old/low-value memories
+    """
+
+    def __init__(
+        self,
+        session: AsyncSession,
+        config: ConsolidationConfig | None = None,
+        embedding_generator: Any | None = None,
+    ) -> None:
+        """
+        Initialize consolidation service.
+
+        Args:
+            session: Database session
+            config: Consolidation configuration
+            embedding_generator: Optional embedding generator
+        """
+        self._session = session
+        self._config = config or ConsolidationConfig()
+        self._embedding_generator = embedding_generator
+        self._fact_extractor: FactExtractor = get_fact_extractor()
+
+        # Memory services (lazy initialized)
+        self._episodic: EpisodicMemory | None = None
+        self._semantic: SemanticMemory | None = None
+        self._procedural: ProceduralMemory | None = None
+
+    async def _get_episodic(self) -> EpisodicMemory:
+        """Get or create episodic memory service."""
+        if self._episodic is None:
+            self._episodic = await EpisodicMemory.create(
+                self._session, self._embedding_generator
+            )
+        return self._episodic
+
+    async def _get_semantic(self) -> SemanticMemory:
+        """Get or create semantic memory service."""
+        if self._semantic is None:
+            self._semantic = await SemanticMemory.create(
+                self._session, self._embedding_generator
+            )
+        return self._semantic
+
+    async def _get_procedural(self) -> ProceduralMemory:
+        """Get or create procedural memory service."""
+        if self._procedural is None:
+            self._procedural = await ProceduralMemory.create(
+                self._session, self._embedding_generator
+            )
+        return self._procedural
+
+    # =========================================================================
+    # Working -> Episodic Consolidation
+    # =========================================================================
+
+    async def consolidate_session(
+        self,
+        working_memory: WorkingMemory,
+        project_id: UUID,
+        session_id: str,
+        task_type: str = "session_task",
+        agent_instance_id: UUID | None = None,
+        agent_type_id: UUID | None = None,
+    ) -> SessionConsolidationResult:
+        """
+        Consolidate a session's working memory to episodic memory.
+
+        Called at session end to transfer relevant session data
+        into a persistent episode.
+
+        Args:
+            working_memory: The session's working memory
+            project_id: Project ID
+            session_id: Session ID
+            task_type: Type of task performed
+            agent_instance_id: Optional agent instance
+            agent_type_id: Optional agent type
+
+        Returns:
+            SessionConsolidationResult with outcome details
+        """
+        result = SessionConsolidationResult(session_id=session_id)
+
+        try:
+            # Get task state
+            task_state = await working_memory.get_task_state()
+
+            # Check if there's enough content to consolidate
+            if not self._should_consolidate_session(task_state):
+                logger.debug(
+                    f"Skipping consolidation for session {session_id}: insufficient content"
+                )
+                return result
+
+            # Gather scratchpad entries
+            scratchpad = await working_memory.get_scratchpad()
+            result.scratchpad_entries = len(scratchpad)
+
+            # Gather user variables
+            all_data = await working_memory.get_all()
+            result.variables_captured = len(all_data)
+
+            # Determine outcome
+            outcome = self._determine_session_outcome(task_state)
+
+            # Build actions from scratchpad and variables
+            actions = self._build_actions_from_session(scratchpad, all_data, task_state)
+
+            # Build context summary
+            context_summary = self._build_context_summary(task_state, all_data)
+
+            # Extract lessons learned
+            lessons = self._extract_session_lessons(task_state, outcome)
+
+            # Calculate importance
+            importance = self._calculate_session_importance(
+                task_state, outcome, actions
+            )
+
+            # Create episode
+            episode_data = EpisodeCreate(
+                project_id=project_id,
+                session_id=session_id,
+                task_type=task_type,
+                task_description=task_state.description
+                if task_state
+                else "Session task",
+                actions=actions,
+                context_summary=context_summary,
+                outcome=outcome,
+                outcome_details=task_state.status if task_state else "",
+                duration_seconds=self._calculate_duration(task_state),
+                tokens_used=0,  # Would need to track this in working memory
+                lessons_learned=lessons,
+                importance_score=importance,
+                agent_instance_id=agent_instance_id,
+                agent_type_id=agent_type_id,
+            )
+
+            episodic = await self._get_episodic()
+            episode = await episodic.record_episode(episode_data)
+
+            result.episode_created = True
+            result.episode_id = episode.id
+
+            logger.info(
+                f"Consolidated session {session_id} to episode {episode.id} "
+                f"({len(actions)} actions, outcome={outcome.value})"
+            )
+
+        except Exception as e:
+            result.error = str(e)
+            logger.exception(f"Failed to consolidate session {session_id}")
+
+        return result
+
+    def _should_consolidate_session(self, task_state: TaskState | None) -> bool:
+        """Check if session has enough content to consolidate."""
+        if task_state is None:
+            return False
+
+        # Check minimum steps
+        if task_state.current_step < self._config.min_steps_for_episode:
+            return False
+
+        return True
+
+    def _determine_session_outcome(self, task_state: TaskState | None) -> Outcome:
+        """Determine outcome from task state."""
+        if task_state is None:
+            return Outcome.PARTIAL
+
+        status = task_state.status.lower() if task_state.status else ""
+        progress = task_state.progress_percent
+
+        if "success" in status or "complete" in status or progress >= 100:
+            return Outcome.SUCCESS
+        if "fail" in status or "error" in status:
+            return Outcome.FAILURE
+        if progress >= 50:
+            return Outcome.PARTIAL
+        return Outcome.FAILURE
+
+    def _build_actions_from_session(
+        self,
+        scratchpad: list[str],
+        variables: dict[str, Any],
+        task_state: TaskState | None,
+    ) -> list[dict[str, Any]]:
+        """Build action list from session data."""
+        actions: list[dict[str, Any]] = []
+
+        # Add scratchpad entries as actions
+        for i, entry in enumerate(scratchpad):
+            actions.append(
+                {
+                    "step": i + 1,
+                    "type": "reasoning",
+                    "content": entry[:500],  # Truncate long entries
+                }
+            )
+
+        # Add final state
+        if task_state:
+            actions.append(
+                {
+                    "step": len(scratchpad) + 1,
+                    "type": "final_state",
+                    "current_step": task_state.current_step,
+                    "total_steps": task_state.total_steps,
+                    "progress": task_state.progress_percent,
+                    "status": task_state.status,
+                }
+            )
+
+        return actions
+
+    def _build_context_summary(
+        self,
+        task_state: TaskState | None,
+        variables: dict[str, Any],
+    ) -> str:
+        """Build context summary from session data."""
+        parts = []
+
+        if task_state:
+            parts.append(f"Task: {task_state.description}")
+            parts.append(f"Progress: {task_state.progress_percent:.1f}%")
+            parts.append(f"Steps: {task_state.current_step}/{task_state.total_steps}")
+
+        # Include key variables
+        key_vars = {k: v for k, v in variables.items() if len(str(v)) < 100}
+        if key_vars:
+            var_str = ", ".join(f"{k}={v}" for k, v in list(key_vars.items())[:5])
+            parts.append(f"Variables: {var_str}")
+
+        return "; ".join(parts) if parts else "Session completed"
+
+    def _extract_session_lessons(
+        self,
+        task_state: TaskState | None,
+        outcome: Outcome,
+    ) -> list[str]:
+        """Extract lessons from session."""
+        lessons: list[str] = []
+
+        if task_state and task_state.status:
+            if outcome == Outcome.FAILURE:
+                lessons.append(
+                    f"Task failed at step {task_state.current_step}: {task_state.status}"
+                )
+            elif outcome == Outcome.SUCCESS:
+                lessons.append(
+                    f"Successfully completed in {task_state.current_step} steps"
+                )
+
+        return lessons
+
+    def _calculate_session_importance(
+        self,
+        task_state: TaskState | None,
+        outcome: Outcome,
+        actions: list[dict[str, Any]],
+    ) -> float:
+        """Calculate importance score for session."""
+        score = 0.5  # Base score
+
+        # Failures are important to learn from
+        if outcome == Outcome.FAILURE:
+            score += 0.3
+
+        # Many steps means complex task
+        if task_state and task_state.total_steps >= 5:
+            score += 0.1
+
+        # Many actions means detailed reasoning
+        if len(actions) >= 5:
+            score += 0.1
+
+        return min(1.0, score)
+
+    def _calculate_duration(self, task_state: TaskState | None) -> float:
+        """Calculate session duration."""
+        if task_state is None:
+            return 0.0
+
+        if task_state.started_at and task_state.updated_at:
+            delta = task_state.updated_at - task_state.started_at
+            return delta.total_seconds()
+
+        return 0.0
+
+    # =========================================================================
+    # Episodic -> Semantic Consolidation
+    # =========================================================================
+
+    async def consolidate_episodes_to_facts(
+        self,
+        project_id: UUID,
+        since: datetime | None = None,
+        limit: int | None = None,
+    ) -> ConsolidationResult:
+        """
+        Extract facts from episodic memories to semantic memory.
+
+        Args:
+            project_id: Project to consolidate
+            since: Only process episodes since this time
+            limit: Maximum episodes to process
+
+        Returns:
+            ConsolidationResult with extraction statistics
+        """
+        start_time = datetime.now(UTC)
+        result = ConsolidationResult(
+            source_type="episodic",
+            target_type="semantic",
+        )
+
+        try:
+            episodic = await self._get_episodic()
+            semantic = await self._get_semantic()
+
+            # Get episodes to process
+            since_time = since or datetime.now(UTC) - timedelta(days=1)
+            episodes = await episodic.get_recent(
+                project_id,
+                limit=limit or self._config.batch_size,
+                since=since_time,
+            )
+
+            for episode in episodes:
+                result.items_processed += 1
+
+                try:
+                    # Extract facts using the extractor
+                    extracted_facts = self._fact_extractor.extract_from_episode(episode)
+
+                    for extracted_fact in extracted_facts:
+                        if (
+                            extracted_fact.confidence
+                            < self._config.min_confidence_for_fact
+                        ):
+                            result.items_skipped += 1
+                            continue
+
+                        # Create fact (store_fact handles deduplication/reinforcement)
+                        fact_create = extracted_fact.to_fact_create(
+                            project_id=project_id,
+                            source_episode_ids=[episode.id],
+                        )
+
+                        # store_fact automatically reinforces if fact already exists
+                        fact = await semantic.store_fact(fact_create)
+
+                        # Check if this was a new fact or reinforced existing
+                        if fact.reinforcement_count == 1:
+                            result.items_created += 1
+                        else:
+                            result.items_updated += 1
+
+                except Exception as e:
+                    result.errors.append(f"Episode {episode.id}: {e}")
+                    logger.warning(
+                        f"Failed to extract facts from episode {episode.id}: {e}"
+                    )
+
+        except Exception as e:
+            result.errors.append(f"Consolidation failed: {e}")
+            logger.exception("Failed episodic -> semantic consolidation")
+
+        result.duration_seconds = (datetime.now(UTC) - start_time).total_seconds()
+
+        logger.info(
+            f"Episodic -> Semantic consolidation: "
+            f"{result.items_processed} processed, "
+            f"{result.items_created} created, "
+            f"{result.items_updated} reinforced"
+        )
+
+        return result
+
+    # =========================================================================
+    # Episodic -> Procedural Consolidation
+    # =========================================================================
+
+    async def consolidate_episodes_to_procedures(
+        self,
+        project_id: UUID,
+        agent_type_id: UUID | None = None,
+        since: datetime | None = None,
+    ) -> ConsolidationResult:
+        """
+        Learn procedures from patterns in episodic memories.
+
+        Identifies recurring successful patterns and creates/updates
+        procedures to capture them.
+
+        Args:
+            project_id: Project to consolidate
+            agent_type_id: Optional filter by agent type
+            since: Only process episodes since this time
+
+        Returns:
+            ConsolidationResult with procedure statistics
+        """
+        start_time = datetime.now(UTC)
+        result = ConsolidationResult(
+            source_type="episodic",
+            target_type="procedural",
+        )
+
+        try:
+            episodic = await self._get_episodic()
+            procedural = await self._get_procedural()
+
+            # Get successful episodes
+            since_time = since or datetime.now(UTC) - timedelta(days=7)
+            episodes = await episodic.get_by_outcome(
+                project_id,
+                outcome=Outcome.SUCCESS,
+                limit=self._config.batch_size,
+                agent_instance_id=None,  # Get all agent instances
+            )
+
+            # Group by task type
+            task_groups: dict[str, list[Episode]] = {}
+            for episode in episodes:
+                if episode.occurred_at >= since_time:
+                    if episode.task_type not in task_groups:
+                        task_groups[episode.task_type] = []
+                    task_groups[episode.task_type].append(episode)
+
+            result.items_processed = len(episodes)
+
+            # Process each task type group
+            for task_type, group in task_groups.items():
+                if len(group) < self._config.min_episodes_for_procedure:
+                    result.items_skipped += len(group)
+                    continue
+
+                try:
+                    procedure_result = await self._learn_procedure_from_episodes(
+                        procedural,
+                        project_id,
+                        agent_type_id,
+                        task_type,
+                        group,
+                    )
+
+                    if procedure_result == "created":
+                        result.items_created += 1
+                    elif procedure_result == "updated":
+                        result.items_updated += 1
+                    else:
+                        result.items_skipped += 1
+
+                except Exception as e:
+                    result.errors.append(f"Task type '{task_type}': {e}")
+                    logger.warning(f"Failed to learn procedure for '{task_type}': {e}")
+
+        except Exception as e:
+            result.errors.append(f"Consolidation failed: {e}")
+            logger.exception("Failed episodic -> procedural consolidation")
+
+        result.duration_seconds = (datetime.now(UTC) - start_time).total_seconds()
+
+        logger.info(
+            f"Episodic -> Procedural consolidation: "
+            f"{result.items_processed} processed, "
+            f"{result.items_created} created, "
+            f"{result.items_updated} updated"
+        )
+
+        return result
+
+    async def _learn_procedure_from_episodes(
+        self,
+        procedural: ProceduralMemory,
+        project_id: UUID,
+        agent_type_id: UUID | None,
+        task_type: str,
+        episodes: list[Episode],
+    ) -> str:
+        """Learn or update a procedure from a set of episodes."""
+        # Calculate success rate for this pattern
+        success_count = sum(1 for e in episodes if e.outcome == Outcome.SUCCESS)
+        total_count = len(episodes)
+        success_rate = success_count / total_count if total_count > 0 else 0
+
+        if success_rate < self._config.min_success_rate_for_procedure:
+            return "skipped"
+
+        # Extract common steps from episodes
+        steps = self._extract_common_steps(episodes)
+
+        if len(steps) < self._config.min_steps_for_procedure:
+            return "skipped"
+
+        # Check for existing procedure
+        matching = await procedural.find_matching(
+            context=task_type,
+            project_id=project_id,
+            agent_type_id=agent_type_id,
+            limit=1,
+        )
+
+        if matching:
+            # Update existing procedure with new success
+            await procedural.record_outcome(
+                matching[0].id,
+                success=True,
+            )
+            return "updated"
+        else:
+            # Create new procedure
+            # Note: success_count starts at 1 in record_procedure
+            procedure_data = ProcedureCreate(
+                project_id=project_id,
+                agent_type_id=agent_type_id,
+                name=f"Procedure for {task_type}",
+                trigger_pattern=task_type,
+                steps=steps,
+            )
+            await procedural.record_procedure(procedure_data)
+            return "created"
+
+    def _extract_common_steps(self, episodes: list[Episode]) -> list[dict[str, Any]]:
+        """Extract common action steps from multiple episodes."""
+        # Simple heuristic: take the steps from the most successful episode
+        # with the most detailed actions
+
+        best_episode = max(
+            episodes,
+            key=lambda e: (
+                e.outcome == Outcome.SUCCESS,
+                len(e.actions),
+                e.importance_score,
+            ),
+        )
+
+        steps: list[dict[str, Any]] = []
+        for i, action in enumerate(best_episode.actions):
+            step = {
+                "order": i + 1,
+                "action": action.get("type", "action"),
+                "description": action.get("content", str(action))[:500],
+                "parameters": action,
+            }
+            steps.append(step)
+
+        return steps
+
+    # =========================================================================
+    # Memory Pruning
+    # =========================================================================
+
+    async def prune_old_episodes(
+        self,
+        project_id: UUID,
+        max_age_days: int | None = None,
+        min_importance: float | None = None,
+    ) -> ConsolidationResult:
+        """
+        Prune old, low-value episodes.
+
+        Args:
+            project_id: Project to prune
+            max_age_days: Maximum age in days (default from config)
+            min_importance: Minimum importance to keep (default from config)
+
+        Returns:
+            ConsolidationResult with pruning statistics
+        """
+        start_time = datetime.now(UTC)
+        result = ConsolidationResult(
+            source_type="episodic",
+            target_type="pruned",
+        )
+
+        max_age = max_age_days or self._config.max_episode_age_days
+        min_imp = min_importance or self._config.min_importance_to_keep
+        cutoff_date = datetime.now(UTC) - timedelta(days=max_age)
+
+        try:
+            episodic = await self._get_episodic()
+
+            # Get old episodes
+            # Note: In production, this would use a more efficient query
+            all_episodes = await episodic.get_recent(
+                project_id,
+                limit=self._config.batch_size * 10,
+                since=cutoff_date - timedelta(days=365),  # Search past year
+            )
+
+            for episode in all_episodes:
+                result.items_processed += 1
+
+                # Check if should be pruned
+                if not self._should_prune_episode(episode, cutoff_date, min_imp):
+                    result.items_skipped += 1
+                    continue
+
+                try:
+                    deleted = await episodic.delete(episode.id)
+                    if deleted:
+                        result.items_pruned += 1
+                    else:
+                        result.items_skipped += 1
+                except Exception as e:
+                    result.errors.append(f"Episode {episode.id}: {e}")
+
+        except Exception as e:
+            result.errors.append(f"Pruning failed: {e}")
+            logger.exception("Failed episode pruning")
+
+        result.duration_seconds = (datetime.now(UTC) - start_time).total_seconds()
+
+        logger.info(
+            f"Episode pruning: {result.items_processed} processed, "
+            f"{result.items_pruned} pruned"
+        )
+
+        return result
+
+    def _should_prune_episode(
+        self,
+        episode: Episode,
+        cutoff_date: datetime,
+        min_importance: float,
+    ) -> bool:
+        """Determine if an episode should be pruned."""
+        # Keep recent episodes
+        if episode.occurred_at >= cutoff_date:
+            return False
+
+        # Keep failures if configured
+        if self._config.keep_all_failures and episode.outcome == Outcome.FAILURE:
+            return False
+
+        # Keep episodes with lessons if configured
+        if self._config.keep_all_with_lessons and episode.lessons_learned:
+            return False
+
+        # Keep high-importance episodes
+        if episode.importance_score >= min_importance:
+            return False
+
+        return True
+
+    # =========================================================================
+    # Nightly Consolidation
+    # =========================================================================
+
+    async def run_nightly_consolidation(
+        self,
+        project_id: UUID,
+        agent_type_id: UUID | None = None,
+    ) -> NightlyConsolidationResult:
+        """
+        Run full nightly consolidation workflow.
+
+        This includes:
+        1. Extract facts from recent episodes
+        2. Learn procedures from successful patterns
+        3. Prune old, low-value memories
+
+        Args:
+            project_id: Project to consolidate
+            agent_type_id: Optional agent type filter
+
+        Returns:
+            NightlyConsolidationResult with all outcomes
+        """
+        result = NightlyConsolidationResult(started_at=datetime.now(UTC))
+
+        logger.info(f"Starting nightly consolidation for project {project_id}")
+
+        try:
+            # Step 1: Episodic -> Semantic (last 24 hours)
+            since_yesterday = datetime.now(UTC) - timedelta(days=1)
+            result.episodic_to_semantic = await self.consolidate_episodes_to_facts(
+                project_id=project_id,
+                since=since_yesterday,
+            )
+            result.total_facts_created = result.episodic_to_semantic.items_created
+
+            # Step 2: Episodic -> Procedural (last 7 days)
+            since_week = datetime.now(UTC) - timedelta(days=7)
+            result.episodic_to_procedural = (
+                await self.consolidate_episodes_to_procedures(
+                    project_id=project_id,
+                    agent_type_id=agent_type_id,
+                    since=since_week,
+                )
+            )
+            result.total_procedures_created = (
+                result.episodic_to_procedural.items_created
+            )
+
+            # Step 3: Prune old memories
+            result.pruning = await self.prune_old_episodes(project_id=project_id)
+            result.total_pruned = result.pruning.items_pruned
+
+            # Calculate totals
+            result.total_episodes_processed = (
+                result.episodic_to_semantic.items_processed
+                if result.episodic_to_semantic
+                else 0
+            ) + (
+                result.episodic_to_procedural.items_processed
+                if result.episodic_to_procedural
+                else 0
+            )
+
+            # Collect all errors
+            if result.episodic_to_semantic and result.episodic_to_semantic.errors:
+                result.errors.extend(result.episodic_to_semantic.errors)
+            if result.episodic_to_procedural and result.episodic_to_procedural.errors:
+                result.errors.extend(result.episodic_to_procedural.errors)
+            if result.pruning and result.pruning.errors:
+                result.errors.extend(result.pruning.errors)
+
+        except Exception as e:
+            result.errors.append(f"Nightly consolidation failed: {e}")
+            logger.exception("Nightly consolidation failed")
+
+        result.completed_at = datetime.now(UTC)
+
+        duration = (result.completed_at - result.started_at).total_seconds()
+        logger.info(
+            f"Nightly consolidation completed in {duration:.1f}s: "
+            f"{result.total_facts_created} facts, "
+            f"{result.total_procedures_created} procedures, "
+            f"{result.total_pruned} pruned"
+        )
+
+        return result
+
+
+# Factory function - no singleton to avoid stale session issues
+async def get_consolidation_service(
+    session: AsyncSession,
+    config: ConsolidationConfig | None = None,
+) -> MemoryConsolidationService:
+    """
+    Create a memory consolidation service for the given session.
+
+    Note: This creates a new instance each time to avoid stale session issues.
+    The service is lightweight and safe to recreate per-request.
+
+    Args:
+        session: Database session (must be active)
+        config: Optional configuration
+
+    Returns:
+        MemoryConsolidationService instance
+    """
+    return MemoryConsolidationService(session=session, config=config)

backend/app/services/memory/episodic/__init__.py

@@ -0,0 +1,17 @@
+# app/services/memory/episodic/__init__.py
+"""
+Episodic Memory Package.
+
+Provides experiential memory storage and retrieval for agent learning.
+"""
+
+from .memory import EpisodicMemory
+from .recorder import EpisodeRecorder
+from .retrieval import EpisodeRetriever, RetrievalStrategy
+
+__all__ = [
+    "EpisodeRecorder",
+    "EpisodeRetriever",
+    "EpisodicMemory",
+    "RetrievalStrategy",
+]

backend/app/services/memory/episodic/memory.py

@@ -0,0 +1,490 @@
+# app/services/memory/episodic/memory.py
+"""
+Episodic Memory Implementation.
+
+Provides experiential memory storage and retrieval for agent learning.
+Combines episode recording and retrieval into a unified interface.
+"""
+
+import logging
+from datetime import datetime
+from typing import Any
+from uuid import UUID
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.services.memory.types import Episode, EpisodeCreate, Outcome, RetrievalResult
+
+from .recorder import EpisodeRecorder
+from .retrieval import EpisodeRetriever, RetrievalStrategy
+
+logger = logging.getLogger(__name__)
+
+
+class EpisodicMemory:
+    """
+    Episodic Memory Service.
+
+    Provides experiential memory for agent learning:
+    - Record task completions with context
+    - Store failures with error context
+    - Retrieve by semantic similarity
+    - Retrieve by recency, outcome, task type
+    - Track importance scores
+    - Extract lessons learned
+
+    Performance target: <100ms P95 for retrieval
+    """
+
+    def __init__(
+        self,
+        session: AsyncSession,
+        embedding_generator: Any | None = None,
+    ) -> None:
+        """
+        Initialize episodic memory.
+
+        Args:
+            session: Database session
+            embedding_generator: Optional embedding generator for semantic search
+        """
+        self._session = session
+        self._embedding_generator = embedding_generator
+        self._recorder = EpisodeRecorder(session, embedding_generator)
+        self._retriever = EpisodeRetriever(session, embedding_generator)
+
+    @classmethod
+    async def create(
+        cls,
+        session: AsyncSession,
+        embedding_generator: Any | None = None,
+    ) -> "EpisodicMemory":
+        """
+        Factory method to create EpisodicMemory.
+
+        Args:
+            session: Database session
+            embedding_generator: Optional embedding generator
+
+        Returns:
+            Configured EpisodicMemory instance
+        """
+        return cls(session=session, embedding_generator=embedding_generator)
+
+    # =========================================================================
+    # Recording Operations
+    # =========================================================================
+
+    async def record_episode(self, episode: EpisodeCreate) -> Episode:
+        """
+        Record a new episode.
+
+        Args:
+            episode: Episode data to record
+
+        Returns:
+            The created episode with assigned ID
+        """
+        return await self._recorder.record(episode)
+
+    async def record_success(
+        self,
+        project_id: UUID,
+        session_id: str,
+        task_type: str,
+        task_description: str,
+        actions: list[dict[str, Any]],
+        context_summary: str,
+        outcome_details: str = "",
+        duration_seconds: float = 0.0,
+        tokens_used: int = 0,
+        lessons_learned: list[str] | None = None,
+        agent_instance_id: UUID | None = None,
+        agent_type_id: UUID | None = None,
+    ) -> Episode:
+        """
+        Convenience method to record a successful episode.
+
+        Args:
+            project_id: Project ID
+            session_id: Session ID
+            task_type: Type of task
+            task_description: Task description
+            actions: Actions taken
+            context_summary: Context summary
+            outcome_details: Optional outcome details
+            duration_seconds: Task duration
+            tokens_used: Tokens consumed
+            lessons_learned: Optional lessons
+            agent_instance_id: Optional agent instance
+            agent_type_id: Optional agent type
+
+        Returns:
+            The created episode
+        """
+        episode_data = EpisodeCreate(
+            project_id=project_id,
+            session_id=session_id,
+            task_type=task_type,
+            task_description=task_description,
+            actions=actions,
+            context_summary=context_summary,
+            outcome=Outcome.SUCCESS,
+            outcome_details=outcome_details,
+            duration_seconds=duration_seconds,
+            tokens_used=tokens_used,
+            lessons_learned=lessons_learned or [],
+            agent_instance_id=agent_instance_id,
+            agent_type_id=agent_type_id,
+        )
+        return await self.record_episode(episode_data)
+
+    async def record_failure(
+        self,
+        project_id: UUID,
+        session_id: str,
+        task_type: str,
+        task_description: str,
+        actions: list[dict[str, Any]],
+        context_summary: str,
+        error_details: str,
+        duration_seconds: float = 0.0,
+        tokens_used: int = 0,
+        lessons_learned: list[str] | None = None,
+        agent_instance_id: UUID | None = None,
+        agent_type_id: UUID | None = None,
+    ) -> Episode:
+        """
+        Convenience method to record a failed episode.
+
+        Args:
+            project_id: Project ID
+            session_id: Session ID
+            task_type: Type of task
+            task_description: Task description
+            actions: Actions taken before failure
+            context_summary: Context summary
+            error_details: Error details
+            duration_seconds: Task duration
+            tokens_used: Tokens consumed
+            lessons_learned: Optional lessons from failure
+            agent_instance_id: Optional agent instance
+            agent_type_id: Optional agent type
+
+        Returns:
+            The created episode
+        """
+        episode_data = EpisodeCreate(
+            project_id=project_id,
+            session_id=session_id,
+            task_type=task_type,
+            task_description=task_description,
+            actions=actions,
+            context_summary=context_summary,
+            outcome=Outcome.FAILURE,
+            outcome_details=error_details,
+            duration_seconds=duration_seconds,
+            tokens_used=tokens_used,
+            lessons_learned=lessons_learned or [],
+            agent_instance_id=agent_instance_id,
+            agent_type_id=agent_type_id,
+        )
+        return await self.record_episode(episode_data)
+
+    # =========================================================================
+    # Retrieval Operations
+    # =========================================================================
+
+    async def search_similar(
+        self,
+        project_id: UUID,
+        query: str,
+        limit: int = 10,
+        agent_instance_id: UUID | None = None,
+    ) -> list[Episode]:
+        """
+        Search for semantically similar episodes.
+
+        Args:
+            project_id: Project to search within
+            query: Search query
+            limit: Maximum results
+            agent_instance_id: Optional filter by agent instance
+
+        Returns:
+            List of similar episodes
+        """
+        result = await self._retriever.search_similar(
+            project_id, query, limit, agent_instance_id
+        )
+        return result.items
+
+    async def get_recent(
+        self,
+        project_id: UUID,
+        limit: int = 10,
+        since: datetime | None = None,
+        agent_instance_id: UUID | None = None,
+    ) -> list[Episode]:
+        """
+        Get recent episodes.
+
+        Args:
+            project_id: Project to search within
+            limit: Maximum results
+            since: Optional time filter
+            agent_instance_id: Optional filter by agent instance
+
+        Returns:
+            List of recent episodes
+        """
+        result = await self._retriever.get_recent(
+            project_id, limit, since, agent_instance_id
+        )
+        return result.items
+
+    async def get_by_outcome(
+        self,
+        project_id: UUID,
+        outcome: Outcome,
+        limit: int = 10,
+        agent_instance_id: UUID | None = None,
+    ) -> list[Episode]:
+        """
+        Get episodes by outcome.
+
+        Args:
+            project_id: Project to search within
+            outcome: Outcome to filter by
+            limit: Maximum results
+            agent_instance_id: Optional filter by agent instance
+
+        Returns:
+            List of episodes with specified outcome
+        """
+        result = await self._retriever.get_by_outcome(
+            project_id, outcome, limit, agent_instance_id
+        )
+        return result.items
+
+    async def get_by_task_type(
+        self,
+        project_id: UUID,
+        task_type: str,
+        limit: int = 10,
+        agent_instance_id: UUID | None = None,
+    ) -> list[Episode]:
+        """
+        Get episodes by task type.
+
+        Args:
+            project_id: Project to search within
+            task_type: Task type to filter by
+            limit: Maximum results
+            agent_instance_id: Optional filter by agent instance
+
+        Returns:
+            List of episodes with specified task type
+        """
+        result = await self._retriever.get_by_task_type(
+            project_id, task_type, limit, agent_instance_id
+        )
+        return result.items
+
+    async def get_important(
+        self,
+        project_id: UUID,
+        limit: int = 10,
+        min_importance: float = 0.7,
+        agent_instance_id: UUID | None = None,
+    ) -> list[Episode]:
+        """
+        Get high-importance episodes.
+
+        Args:
+            project_id: Project to search within
+            limit: Maximum results
+            min_importance: Minimum importance score
+            agent_instance_id: Optional filter by agent instance
+
+        Returns:
+            List of important episodes
+        """
+        result = await self._retriever.get_important(
+            project_id, limit, min_importance, agent_instance_id
+        )
+        return result.items
+
+    async def retrieve(
+        self,
+        project_id: UUID,
+        strategy: RetrievalStrategy = RetrievalStrategy.RECENCY,
+        limit: int = 10,
+        **kwargs: Any,
+    ) -> RetrievalResult[Episode]:
+        """
+        Retrieve episodes with full result metadata.
+
+        Args:
+            project_id: Project to search within
+            strategy: Retrieval strategy
+            limit: Maximum results
+            **kwargs: Strategy-specific parameters
+
+        Returns:
+            RetrievalResult with episodes and metadata
+        """
+        return await self._retriever.retrieve(project_id, strategy, limit, **kwargs)
+
+    # =========================================================================
+    # Modification Operations
+    # =========================================================================
+
+    async def get_by_id(self, episode_id: UUID) -> Episode | None:
+        """Get an episode by ID."""
+        return await self._recorder.get_by_id(episode_id)
+
+    async def update_importance(
+        self,
+        episode_id: UUID,
+        importance_score: float,
+    ) -> Episode | None:
+        """
+        Update an episode's importance score.
+
+        Args:
+            episode_id: Episode to update
+            importance_score: New importance score (0.0 to 1.0)
+
+        Returns:
+            Updated episode or None if not found
+        """
+        return await self._recorder.update_importance(episode_id, importance_score)
+
+    async def add_lessons(
+        self,
+        episode_id: UUID,
+        lessons: list[str],
+    ) -> Episode | None:
+        """
+        Add lessons learned to an episode.
+
+        Args:
+            episode_id: Episode to update
+            lessons: Lessons to add
+
+        Returns:
+            Updated episode or None if not found
+        """
+        return await self._recorder.add_lessons(episode_id, lessons)
+
+    async def delete(self, episode_id: UUID) -> bool:
+        """
+        Delete an episode.
+
+        Args:
+            episode_id: Episode to delete
+
+        Returns:
+            True if deleted
+        """
+        return await self._recorder.delete(episode_id)
+
+    # =========================================================================
+    # Summarization
+    # =========================================================================
+
+    async def summarize_episodes(
+        self,
+        episode_ids: list[UUID],
+    ) -> str:
+        """
+        Summarize multiple episodes into a consolidated view.
+
+        Args:
+            episode_ids: Episodes to summarize
+
+        Returns:
+            Summary text
+        """
+        if not episode_ids:
+            return "No episodes to summarize."
+
+        episodes: list[Episode] = []
+        for episode_id in episode_ids:
+            episode = await self.get_by_id(episode_id)
+            if episode:
+                episodes.append(episode)
+
+        if not episodes:
+            return "No episodes found."
+
+        # Build summary
+        lines = [f"Summary of {len(episodes)} episodes:", ""]
+
+        # Outcome breakdown
+        success = sum(1 for e in episodes if e.outcome == Outcome.SUCCESS)
+        failure = sum(1 for e in episodes if e.outcome == Outcome.FAILURE)
+        partial = sum(1 for e in episodes if e.outcome == Outcome.PARTIAL)
+        lines.append(
+            f"Outcomes: {success} success, {failure} failure, {partial} partial"
+        )
+
+        # Task types
+        task_types = {e.task_type for e in episodes}
+        lines.append(f"Task types: {', '.join(sorted(task_types))}")
+
+        # Aggregate lessons
+        all_lessons: list[str] = []
+        for e in episodes:
+            all_lessons.extend(e.lessons_learned)
+
+        if all_lessons:
+            lines.append("")
+            lines.append("Key lessons learned:")
+            # Deduplicate lessons
+            unique_lessons = list(dict.fromkeys(all_lessons))
+            for lesson in unique_lessons[:10]:  # Top 10
+                lines.append(f"  - {lesson}")
+
+        # Duration and tokens
+        total_duration = sum(e.duration_seconds for e in episodes)
+        total_tokens = sum(e.tokens_used for e in episodes)
+        lines.append("")
+        lines.append(f"Total duration: {total_duration:.1f}s")
+        lines.append(f"Total tokens: {total_tokens:,}")
+
+        return "\n".join(lines)
+
+    # =========================================================================
+    # Statistics
+    # =========================================================================
+
+    async def get_stats(self, project_id: UUID) -> dict[str, Any]:
+        """
+        Get episode statistics for a project.
+
+        Args:
+            project_id: Project to get stats for
+
+        Returns:
+            Dictionary with episode statistics
+        """
+        return await self._recorder.get_stats(project_id)
+
+    async def count(
+        self,
+        project_id: UUID,
+        since: datetime | None = None,
+    ) -> int:
+        """
+        Count episodes for a project.
+
+        Args:
+            project_id: Project to count for
+            since: Optional time filter
+
+        Returns:
+            Number of episodes
+        """
+        return await self._recorder.count_by_project(project_id, since)

backend/app/services/memory/episodic/recorder.py

@@ -0,0 +1,357 @@
+# app/services/memory/episodic/recorder.py
+"""
+Episode Recording.
+
+Handles the creation and storage of episodic memories
+during agent task execution.
+"""
+
+import logging
+from datetime import UTC, datetime
+from typing import Any
+from uuid import UUID, uuid4
+
+from sqlalchemy import select, update
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.memory.enums import EpisodeOutcome
+from app.models.memory.episode import Episode as EpisodeModel
+from app.services.memory.config import get_memory_settings
+from app.services.memory.types import Episode, EpisodeCreate, Outcome
+
+logger = logging.getLogger(__name__)
+
+
+def _outcome_to_db(outcome: Outcome) -> EpisodeOutcome:
+    """Convert service Outcome to database EpisodeOutcome."""
+    return EpisodeOutcome(outcome.value)
+
+
+def _db_to_outcome(db_outcome: EpisodeOutcome) -> Outcome:
+    """Convert database EpisodeOutcome to service Outcome."""
+    return Outcome(db_outcome.value)
+
+
+def _model_to_episode(model: EpisodeModel) -> Episode:
+    """Convert SQLAlchemy model to Episode dataclass."""
+    # SQLAlchemy Column types are inferred as Column[T] by mypy, but at runtime
+    # they return actual values. We use type: ignore to handle this mismatch.
+    return Episode(
+        id=model.id,  # type: ignore[arg-type]
+        project_id=model.project_id,  # type: ignore[arg-type]
+        agent_instance_id=model.agent_instance_id,  # type: ignore[arg-type]
+        agent_type_id=model.agent_type_id,  # type: ignore[arg-type]
+        session_id=model.session_id,  # type: ignore[arg-type]
+        task_type=model.task_type,  # type: ignore[arg-type]
+        task_description=model.task_description,  # type: ignore[arg-type]
+        actions=model.actions or [],  # type: ignore[arg-type]
+        context_summary=model.context_summary,  # type: ignore[arg-type]
+        outcome=_db_to_outcome(model.outcome),  # type: ignore[arg-type]
+        outcome_details=model.outcome_details or "",  # type: ignore[arg-type]
+        duration_seconds=model.duration_seconds,  # type: ignore[arg-type]
+        tokens_used=model.tokens_used,  # type: ignore[arg-type]
+        lessons_learned=model.lessons_learned or [],  # type: ignore[arg-type]
+        importance_score=model.importance_score,  # type: ignore[arg-type]
+        embedding=None,  # Don't expose raw embedding
+        occurred_at=model.occurred_at,  # type: ignore[arg-type]
+        created_at=model.created_at,  # type: ignore[arg-type]
+        updated_at=model.updated_at,  # type: ignore[arg-type]
+    )
+
+
+class EpisodeRecorder:
+    """
+    Records episodes to the database.
+
+    Handles episode creation, importance scoring,
+    and lesson extraction.
+    """
+
+    def __init__(
+        self,
+        session: AsyncSession,
+        embedding_generator: Any | None = None,
+    ) -> None:
+        """
+        Initialize recorder.
+
+        Args:
+            session: Database session
+            embedding_generator: Optional embedding generator for semantic indexing
+        """
+        self._session = session
+        self._embedding_generator = embedding_generator
+        self._settings = get_memory_settings()
+
+    async def record(self, episode_data: EpisodeCreate) -> Episode:
+        """
+        Record a new episode.
+
+        Args:
+            episode_data: Episode data to record
+
+        Returns:
+            The created episode
+        """
+        now = datetime.now(UTC)
+
+        # Calculate importance score if not provided
+        importance = episode_data.importance_score
+        if importance == 0.5:  # Default value, calculate
+            importance = self._calculate_importance(episode_data)
+
+        # Create the model
+        model = EpisodeModel(
+            id=uuid4(),
+            project_id=episode_data.project_id,
+            agent_instance_id=episode_data.agent_instance_id,
+            agent_type_id=episode_data.agent_type_id,
+            session_id=episode_data.session_id,
+            task_type=episode_data.task_type,
+            task_description=episode_data.task_description,
+            actions=episode_data.actions,
+            context_summary=episode_data.context_summary,
+            outcome=_outcome_to_db(episode_data.outcome),
+            outcome_details=episode_data.outcome_details,
+            duration_seconds=episode_data.duration_seconds,
+            tokens_used=episode_data.tokens_used,
+            lessons_learned=episode_data.lessons_learned,
+            importance_score=importance,
+            occurred_at=now,
+            created_at=now,
+            updated_at=now,
+        )
+
+        # Generate embedding if generator available
+        if self._embedding_generator is not None:
+            try:
+                text_for_embedding = self._create_embedding_text(episode_data)
+                embedding = await self._embedding_generator.generate(text_for_embedding)
+                model.embedding = embedding
+            except Exception as e:
+                logger.warning(f"Failed to generate embedding: {e}")
+
+        self._session.add(model)
+        await self._session.flush()
+        await self._session.refresh(model)
+
+        logger.debug(f"Recorded episode {model.id} for task {model.task_type}")
+        return _model_to_episode(model)
+
+    def _calculate_importance(self, episode_data: EpisodeCreate) -> float:
+        """
+        Calculate importance score for an episode.
+
+        Factors:
+        - Outcome: Failures are more important to learn from
+        - Duration: Longer tasks may be more significant
+        - Token usage: Higher usage may indicate complexity
+        - Lessons learned: Episodes with lessons are more valuable
+        """
+        score = 0.5  # Base score
+
+        # Outcome factor
+        if episode_data.outcome == Outcome.FAILURE:
+            score += 0.2  # Failures are important for learning
+        elif episode_data.outcome == Outcome.PARTIAL:
+            score += 0.1
+        # Success is default, no adjustment
+
+        # Lessons learned factor
+        if episode_data.lessons_learned:
+            score += min(0.15, len(episode_data.lessons_learned) * 0.05)
+
+        # Duration factor (longer tasks may be more significant)
+        if episode_data.duration_seconds > 60:
+            score += 0.05
+        if episode_data.duration_seconds > 300:
+            score += 0.05
+
+        # Token usage factor (complex tasks)
+        if episode_data.tokens_used > 1000:
+            score += 0.05
+
+        # Clamp to valid range
+        return min(1.0, max(0.0, score))
+
+    def _create_embedding_text(self, episode_data: EpisodeCreate) -> str:
+        """Create text representation for embedding generation."""
+        parts = [
+            f"Task: {episode_data.task_type}",
+            f"Description: {episode_data.task_description}",
+            f"Context: {episode_data.context_summary}",
+            f"Outcome: {episode_data.outcome.value}",
+        ]
+
+        if episode_data.outcome_details:
+            parts.append(f"Details: {episode_data.outcome_details}")
+
+        if episode_data.lessons_learned:
+            parts.append(f"Lessons: {', '.join(episode_data.lessons_learned)}")
+
+        return "\n".join(parts)
+
+    async def get_by_id(self, episode_id: UUID) -> Episode | None:
+        """Get an episode by ID."""
+        query = select(EpisodeModel).where(EpisodeModel.id == episode_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+
+        if model is None:
+            return None
+
+        return _model_to_episode(model)
+
+    async def update_importance(
+        self,
+        episode_id: UUID,
+        importance_score: float,
+    ) -> Episode | None:
+        """
+        Update the importance score of an episode.
+
+        Args:
+            episode_id: Episode to update
+            importance_score: New importance score (0.0 to 1.0)
+
+        Returns:
+            Updated episode or None if not found
+        """
+        # Validate score
+        importance_score = min(1.0, max(0.0, importance_score))
+
+        stmt = (
+            update(EpisodeModel)
+            .where(EpisodeModel.id == episode_id)
+            .values(
+                importance_score=importance_score,
+                updated_at=datetime.now(UTC),
+            )
+            .returning(EpisodeModel)
+        )
+
+        result = await self._session.execute(stmt)
+        model = result.scalar_one_or_none()
+
+        if model is None:
+            return None
+
+        await self._session.flush()
+        return _model_to_episode(model)
+
+    async def add_lessons(
+        self,
+        episode_id: UUID,
+        lessons: list[str],
+    ) -> Episode | None:
+        """
+        Add lessons learned to an episode.
+
+        Args:
+            episode_id: Episode to update
+            lessons: New lessons to add
+
+        Returns:
+            Updated episode or None if not found
+        """
+        # Get current episode
+        query = select(EpisodeModel).where(EpisodeModel.id == episode_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+
+        if model is None:
+            return None
+
+        # Append lessons
+        current_lessons: list[str] = model.lessons_learned or []  # type: ignore[assignment]
+        updated_lessons = current_lessons + lessons
+
+        stmt = (
+            update(EpisodeModel)
+            .where(EpisodeModel.id == episode_id)
+            .values(
+                lessons_learned=updated_lessons,
+                updated_at=datetime.now(UTC),
+            )
+            .returning(EpisodeModel)
+        )
+
+        result = await self._session.execute(stmt)
+        model = result.scalar_one_or_none()
+        await self._session.flush()
+
+        return _model_to_episode(model) if model else None
+
+    async def delete(self, episode_id: UUID) -> bool:
+        """
+        Delete an episode.
+
+        Args:
+            episode_id: Episode to delete
+
+        Returns:
+            True if deleted
+        """
+        query = select(EpisodeModel).where(EpisodeModel.id == episode_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+
+        if model is None:
+            return False
+
+        await self._session.delete(model)
+        await self._session.flush()
+        return True
+
+    async def count_by_project(
+        self,
+        project_id: UUID,
+        since: datetime | None = None,
+    ) -> int:
+        """Count episodes for a project."""
+        query = select(EpisodeModel).where(EpisodeModel.project_id == project_id)
+        if since is not None:
+            query = query.where(EpisodeModel.occurred_at >= since)
+
+        result = await self._session.execute(query)
+        return len(list(result.scalars().all()))
+
+    async def get_stats(self, project_id: UUID) -> dict[str, Any]:
+        """
+        Get statistics for a project's episodes.
+
+        Returns:
+            Dictionary with episode statistics
+        """
+        query = select(EpisodeModel).where(EpisodeModel.project_id == project_id)
+        result = await self._session.execute(query)
+        episodes = list(result.scalars().all())
+
+        if not episodes:
+            return {
+                "total_count": 0,
+                "success_count": 0,
+                "failure_count": 0,
+                "partial_count": 0,
+                "avg_importance": 0.0,
+                "avg_duration": 0.0,
+                "total_tokens": 0,
+            }
+
+        success_count = sum(1 for e in episodes if e.outcome == EpisodeOutcome.SUCCESS)
+        failure_count = sum(1 for e in episodes if e.outcome == EpisodeOutcome.FAILURE)
+        partial_count = sum(1 for e in episodes if e.outcome == EpisodeOutcome.PARTIAL)
+
+        avg_importance = sum(e.importance_score for e in episodes) / len(episodes)
+        avg_duration = sum(e.duration_seconds for e in episodes) / len(episodes)
+        total_tokens = sum(e.tokens_used for e in episodes)
+
+        return {
+            "total_count": len(episodes),
+            "success_count": success_count,
+            "failure_count": failure_count,
+            "partial_count": partial_count,
+            "avg_importance": avg_importance,
+            "avg_duration": avg_duration,
+            "total_tokens": total_tokens,
+        }

backend/app/services/memory/episodic/retrieval.py

@@ -0,0 +1,503 @@
+# app/services/memory/episodic/retrieval.py
+"""
+Episode Retrieval Strategies.
+
+Provides different retrieval strategies for finding relevant episodes:
+- Semantic similarity (vector search)
+- Recency-based
+- Outcome-based filtering
+- Importance-based ranking
+"""
+
+import logging
+import time
+from abc import ABC, abstractmethod
+from datetime import datetime
+from enum import Enum
+from typing import Any
+from uuid import UUID
+
+from sqlalchemy import and_, desc, select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.memory.enums import EpisodeOutcome
+from app.models.memory.episode import Episode as EpisodeModel
+from app.services.memory.types import Episode, Outcome, RetrievalResult
+
+logger = logging.getLogger(__name__)
+
+
+class RetrievalStrategy(str, Enum):
+    """Retrieval strategy types."""
+
+    SEMANTIC = "semantic"
+    RECENCY = "recency"
+    OUTCOME = "outcome"
+    IMPORTANCE = "importance"
+    HYBRID = "hybrid"
+
+
+def _model_to_episode(model: EpisodeModel) -> Episode:
+    """Convert SQLAlchemy model to Episode dataclass."""
+    # SQLAlchemy Column types are inferred as Column[T] by mypy, but at runtime
+    # they return actual values. We use type: ignore to handle this mismatch.
+    return Episode(
+        id=model.id,  # type: ignore[arg-type]
+        project_id=model.project_id,  # type: ignore[arg-type]
+        agent_instance_id=model.agent_instance_id,  # type: ignore[arg-type]
+        agent_type_id=model.agent_type_id,  # type: ignore[arg-type]
+        session_id=model.session_id,  # type: ignore[arg-type]
+        task_type=model.task_type,  # type: ignore[arg-type]
+        task_description=model.task_description,  # type: ignore[arg-type]
+        actions=model.actions or [],  # type: ignore[arg-type]
+        context_summary=model.context_summary,  # type: ignore[arg-type]
+        outcome=Outcome(model.outcome.value),
+        outcome_details=model.outcome_details or "",  # type: ignore[arg-type]
+        duration_seconds=model.duration_seconds,  # type: ignore[arg-type]
+        tokens_used=model.tokens_used,  # type: ignore[arg-type]
+        lessons_learned=model.lessons_learned or [],  # type: ignore[arg-type]
+        importance_score=model.importance_score,  # type: ignore[arg-type]
+        embedding=None,  # Don't expose raw embedding
+        occurred_at=model.occurred_at,  # type: ignore[arg-type]
+        created_at=model.created_at,  # type: ignore[arg-type]
+        updated_at=model.updated_at,  # type: ignore[arg-type]
+    )
+
+
+class BaseRetriever(ABC):
+    """Abstract base class for episode retrieval strategies."""
+
+    @abstractmethod
+    async def retrieve(
+        self,
+        session: AsyncSession,
+        project_id: UUID,
+        limit: int = 10,
+        **kwargs: Any,
+    ) -> RetrievalResult[Episode]:
+        """Retrieve episodes based on the strategy."""
+        ...
+
+
+class RecencyRetriever(BaseRetriever):
+    """Retrieves episodes by recency (most recent first)."""
+
+    async def retrieve(
+        self,
+        session: AsyncSession,
+        project_id: UUID,
+        limit: int = 10,
+        *,
+        since: datetime | None = None,
+        agent_instance_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> RetrievalResult[Episode]:
+        """Retrieve most recent episodes."""
+        start_time = time.perf_counter()
+
+        query = (
+            select(EpisodeModel)
+            .where(EpisodeModel.project_id == project_id)
+            .order_by(desc(EpisodeModel.occurred_at))
+            .limit(limit)
+        )
+
+        if since is not None:
+            query = query.where(EpisodeModel.occurred_at >= since)
+
+        if agent_instance_id is not None:
+            query = query.where(EpisodeModel.agent_instance_id == agent_instance_id)
+
+        result = await session.execute(query)
+        models = list(result.scalars().all())
+
+        # Get total count
+        count_query = select(EpisodeModel).where(EpisodeModel.project_id == project_id)
+        if since is not None:
+            count_query = count_query.where(EpisodeModel.occurred_at >= since)
+        count_result = await session.execute(count_query)
+        total_count = len(list(count_result.scalars().all()))
+
+        latency_ms = (time.perf_counter() - start_time) * 1000
+
+        return RetrievalResult(
+            items=[_model_to_episode(m) for m in models],
+            total_count=total_count,
+            query="recency",
+            retrieval_type=RetrievalStrategy.RECENCY.value,
+            latency_ms=latency_ms,
+            metadata={"since": since.isoformat() if since else None},
+        )
+
+
+class OutcomeRetriever(BaseRetriever):
+    """Retrieves episodes filtered by outcome."""
+
+    async def retrieve(
+        self,
+        session: AsyncSession,
+        project_id: UUID,
+        limit: int = 10,
+        *,
+        outcome: Outcome | None = None,
+        agent_instance_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> RetrievalResult[Episode]:
+        """Retrieve episodes by outcome."""
+        start_time = time.perf_counter()
+
+        query = (
+            select(EpisodeModel)
+            .where(EpisodeModel.project_id == project_id)
+            .order_by(desc(EpisodeModel.occurred_at))
+            .limit(limit)
+        )
+
+        if outcome is not None:
+            db_outcome = EpisodeOutcome(outcome.value)
+            query = query.where(EpisodeModel.outcome == db_outcome)
+
+        if agent_instance_id is not None:
+            query = query.where(EpisodeModel.agent_instance_id == agent_instance_id)
+
+        result = await session.execute(query)
+        models = list(result.scalars().all())
+
+        # Get total count
+        count_query = select(EpisodeModel).where(EpisodeModel.project_id == project_id)
+        if outcome is not None:
+            count_query = count_query.where(
+                EpisodeModel.outcome == EpisodeOutcome(outcome.value)
+            )
+        count_result = await session.execute(count_query)
+        total_count = len(list(count_result.scalars().all()))
+
+        latency_ms = (time.perf_counter() - start_time) * 1000
+
+        return RetrievalResult(
+            items=[_model_to_episode(m) for m in models],
+            total_count=total_count,
+            query=f"outcome:{outcome.value if outcome else 'all'}",
+            retrieval_type=RetrievalStrategy.OUTCOME.value,
+            latency_ms=latency_ms,
+            metadata={"outcome": outcome.value if outcome else None},
+        )
+
+
+class TaskTypeRetriever(BaseRetriever):
+    """Retrieves episodes filtered by task type."""
+
+    async def retrieve(
+        self,
+        session: AsyncSession,
+        project_id: UUID,
+        limit: int = 10,
+        *,
+        task_type: str | None = None,
+        agent_instance_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> RetrievalResult[Episode]:
+        """Retrieve episodes by task type."""
+        start_time = time.perf_counter()
+
+        query = (
+            select(EpisodeModel)
+            .where(EpisodeModel.project_id == project_id)
+            .order_by(desc(EpisodeModel.occurred_at))
+            .limit(limit)
+        )
+
+        if task_type is not None:
+            query = query.where(EpisodeModel.task_type == task_type)
+
+        if agent_instance_id is not None:
+            query = query.where(EpisodeModel.agent_instance_id == agent_instance_id)
+
+        result = await session.execute(query)
+        models = list(result.scalars().all())
+
+        # Get total count
+        count_query = select(EpisodeModel).where(EpisodeModel.project_id == project_id)
+        if task_type is not None:
+            count_query = count_query.where(EpisodeModel.task_type == task_type)
+        count_result = await session.execute(count_query)
+        total_count = len(list(count_result.scalars().all()))
+
+        latency_ms = (time.perf_counter() - start_time) * 1000
+
+        return RetrievalResult(
+            items=[_model_to_episode(m) for m in models],
+            total_count=total_count,
+            query=f"task_type:{task_type or 'all'}",
+            retrieval_type="task_type",
+            latency_ms=latency_ms,
+            metadata={"task_type": task_type},
+        )
+
+
+class ImportanceRetriever(BaseRetriever):
+    """Retrieves episodes ranked by importance score."""
+
+    async def retrieve(
+        self,
+        session: AsyncSession,
+        project_id: UUID,
+        limit: int = 10,
+        *,
+        min_importance: float = 0.0,
+        agent_instance_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> RetrievalResult[Episode]:
+        """Retrieve episodes by importance."""
+        start_time = time.perf_counter()
+
+        query = (
+            select(EpisodeModel)
+            .where(
+                and_(
+                    EpisodeModel.project_id == project_id,
+                    EpisodeModel.importance_score >= min_importance,
+                )
+            )
+            .order_by(desc(EpisodeModel.importance_score))
+            .limit(limit)
+        )
+
+        if agent_instance_id is not None:
+            query = query.where(EpisodeModel.agent_instance_id == agent_instance_id)
+
+        result = await session.execute(query)
+        models = list(result.scalars().all())
+
+        # Get total count
+        count_query = select(EpisodeModel).where(
+            and_(
+                EpisodeModel.project_id == project_id,
+                EpisodeModel.importance_score >= min_importance,
+            )
+        )
+        count_result = await session.execute(count_query)
+        total_count = len(list(count_result.scalars().all()))
+
+        latency_ms = (time.perf_counter() - start_time) * 1000
+
+        return RetrievalResult(
+            items=[_model_to_episode(m) for m in models],
+            total_count=total_count,
+            query=f"importance>={min_importance}",
+            retrieval_type=RetrievalStrategy.IMPORTANCE.value,
+            latency_ms=latency_ms,
+            metadata={"min_importance": min_importance},
+        )
+
+
+class SemanticRetriever(BaseRetriever):
+    """Retrieves episodes by semantic similarity using vector search."""
+
+    def __init__(self, embedding_generator: Any | None = None) -> None:
+        """Initialize with optional embedding generator."""
+        self._embedding_generator = embedding_generator
+
+    async def retrieve(
+        self,
+        session: AsyncSession,
+        project_id: UUID,
+        limit: int = 10,
+        *,
+        query_text: str | None = None,
+        query_embedding: list[float] | None = None,
+        agent_instance_id: UUID | None = None,
+        **kwargs: Any,
+    ) -> RetrievalResult[Episode]:
+        """Retrieve episodes by semantic similarity."""
+        start_time = time.perf_counter()
+
+        # If no embedding provided, fall back to recency
+        if query_embedding is None and query_text is None:
+            logger.warning(
+                "No query provided for semantic search, falling back to recency"
+            )
+            recency = RecencyRetriever()
+            fallback_result = await recency.retrieve(
+                session, project_id, limit, agent_instance_id=agent_instance_id
+            )
+            latency_ms = (time.perf_counter() - start_time) * 1000
+            return RetrievalResult(
+                items=fallback_result.items,
+                total_count=fallback_result.total_count,
+                query="no_query",
+                retrieval_type=RetrievalStrategy.SEMANTIC.value,
+                latency_ms=latency_ms,
+                metadata={"fallback": "recency", "reason": "no_query"},
+            )
+
+        # Generate embedding if needed
+        embedding = query_embedding
+        if embedding is None and query_text is not None:
+            if self._embedding_generator is not None:
+                embedding = await self._embedding_generator.generate(query_text)
+            else:
+                logger.warning("No embedding generator, falling back to recency")
+                recency = RecencyRetriever()
+                fallback_result = await recency.retrieve(
+                    session, project_id, limit, agent_instance_id=agent_instance_id
+                )
+                latency_ms = (time.perf_counter() - start_time) * 1000
+                return RetrievalResult(
+                    items=fallback_result.items,
+                    total_count=fallback_result.total_count,
+                    query=query_text,
+                    retrieval_type=RetrievalStrategy.SEMANTIC.value,
+                    latency_ms=latency_ms,
+                    metadata={
+                        "fallback": "recency",
+                        "reason": "no_embedding_generator",
+                    },
+                )
+
+        # For now, use recency if vector search not available
+        # TODO: Implement proper pgvector similarity search when integrated
+        logger.debug("Vector search not yet implemented, using recency fallback")
+        recency = RecencyRetriever()
+        result = await recency.retrieve(
+            session, project_id, limit, agent_instance_id=agent_instance_id
+        )
+
+        latency_ms = (time.perf_counter() - start_time) * 1000
+
+        return RetrievalResult(
+            items=result.items,
+            total_count=result.total_count,
+            query=query_text or "embedding",
+            retrieval_type=RetrievalStrategy.SEMANTIC.value,
+            latency_ms=latency_ms,
+            metadata={"fallback": "recency"},
+        )
+
+
+class EpisodeRetriever:
+    """
+    Unified episode retrieval service.
+
+    Provides a single interface for all retrieval strategies.
+    """
+
+    def __init__(
+        self,
+        session: AsyncSession,
+        embedding_generator: Any | None = None,
+    ) -> None:
+        """Initialize retriever with database session."""
+        self._session = session
+        self._retrievers: dict[RetrievalStrategy, BaseRetriever] = {
+            RetrievalStrategy.RECENCY: RecencyRetriever(),
+            RetrievalStrategy.OUTCOME: OutcomeRetriever(),
+            RetrievalStrategy.IMPORTANCE: ImportanceRetriever(),
+            RetrievalStrategy.SEMANTIC: SemanticRetriever(embedding_generator),
+        }
+
+    async def retrieve(
+        self,
+        project_id: UUID,
+        strategy: RetrievalStrategy = RetrievalStrategy.RECENCY,
+        limit: int = 10,
+        **kwargs: Any,
+    ) -> RetrievalResult[Episode]:
+        """
+        Retrieve episodes using the specified strategy.
+
+        Args:
+            project_id: Project to search within
+            strategy: Retrieval strategy to use
+            limit: Maximum number of episodes to return
+            **kwargs: Strategy-specific parameters
+
+        Returns:
+            RetrievalResult containing matching episodes
+        """
+        retriever = self._retrievers.get(strategy)
+        if retriever is None:
+            raise ValueError(f"Unknown retrieval strategy: {strategy}")
+
+        return await retriever.retrieve(self._session, project_id, limit, **kwargs)
+
+    async def get_recent(
+        self,
+        project_id: UUID,
+        limit: int = 10,
+        since: datetime | None = None,
+        agent_instance_id: UUID | None = None,
+    ) -> RetrievalResult[Episode]:
+        """Get recent episodes."""
+        return await self.retrieve(
+            project_id,
+            RetrievalStrategy.RECENCY,
+            limit,
+            since=since,
+            agent_instance_id=agent_instance_id,
+        )
+
+    async def get_by_outcome(
+        self,
+        project_id: UUID,
+        outcome: Outcome,
+        limit: int = 10,
+        agent_instance_id: UUID | None = None,
+    ) -> RetrievalResult[Episode]:
+        """Get episodes by outcome."""
+        return await self.retrieve(
+            project_id,
+            RetrievalStrategy.OUTCOME,
+            limit,
+            outcome=outcome,
+            agent_instance_id=agent_instance_id,
+        )
+
+    async def get_by_task_type(
+        self,
+        project_id: UUID,
+        task_type: str,
+        limit: int = 10,
+        agent_instance_id: UUID | None = None,
+    ) -> RetrievalResult[Episode]:
+        """Get episodes by task type."""
+        retriever = TaskTypeRetriever()
+        return await retriever.retrieve(
+            self._session,
+            project_id,
+            limit,
+            task_type=task_type,
+            agent_instance_id=agent_instance_id,
+        )
+
+    async def get_important(
+        self,
+        project_id: UUID,
+        limit: int = 10,
+        min_importance: float = 0.7,
+        agent_instance_id: UUID | None = None,
+    ) -> RetrievalResult[Episode]:
+        """Get high-importance episodes."""
+        return await self.retrieve(
+            project_id,
+            RetrievalStrategy.IMPORTANCE,
+            limit,
+            min_importance=min_importance,
+            agent_instance_id=agent_instance_id,
+        )
+
+    async def search_similar(
+        self,
+        project_id: UUID,
+        query: str,
+        limit: int = 10,
+        agent_instance_id: UUID | None = None,
+    ) -> RetrievalResult[Episode]:
+        """Search for semantically similar episodes."""
+        return await self.retrieve(
+            project_id,
+            RetrievalStrategy.SEMANTIC,
+            limit,
+            query_text=query,
+            agent_instance_id=agent_instance_id,
+        )

backend/app/services/memory/exceptions.py

@@ -0,0 +1,222 @@
+"""
+Memory System Exceptions
+
+Custom exception classes for the Agent Memory System.
+"""
+
+from typing import Any
+from uuid import UUID
+
+
+class MemoryError(Exception):
+    """Base exception for all memory-related errors."""
+
+    def __init__(
+        self,
+        message: str,
+        *,
+        memory_type: str | None = None,
+        scope_type: str | None = None,
+        scope_id: str | None = None,
+        details: dict[str, Any] | None = None,
+    ) -> None:
+        super().__init__(message)
+        self.message = message
+        self.memory_type = memory_type
+        self.scope_type = scope_type
+        self.scope_id = scope_id
+        self.details = details or {}
+
+
+class MemoryNotFoundError(MemoryError):
+    """Raised when a memory item is not found."""
+
+    def __init__(
+        self,
+        message: str = "Memory not found",
+        *,
+        memory_id: UUID | str | None = None,
+        key: str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.memory_id = memory_id
+        self.key = key
+
+
+class MemoryCapacityError(MemoryError):
+    """Raised when memory capacity limits are exceeded."""
+
+    def __init__(
+        self,
+        message: str = "Memory capacity exceeded",
+        *,
+        current_size: int = 0,
+        max_size: int = 0,
+        item_count: int = 0,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.current_size = current_size
+        self.max_size = max_size
+        self.item_count = item_count
+
+
+class MemoryExpiredError(MemoryError):
+    """Raised when attempting to access expired memory."""
+
+    def __init__(
+        self,
+        message: str = "Memory has expired",
+        *,
+        key: str | None = None,
+        expired_at: str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.key = key
+        self.expired_at = expired_at
+
+
+class MemoryStorageError(MemoryError):
+    """Raised when memory storage operations fail."""
+
+    def __init__(
+        self,
+        message: str = "Memory storage operation failed",
+        *,
+        operation: str | None = None,
+        backend: str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.operation = operation
+        self.backend = backend
+
+
+class MemoryConnectionError(MemoryError):
+    """Raised when memory storage connection fails."""
+
+    def __init__(
+        self,
+        message: str = "Memory connection failed",
+        *,
+        backend: str | None = None,
+        host: str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.backend = backend
+        self.host = host
+
+
+class MemorySerializationError(MemoryError):
+    """Raised when memory serialization/deserialization fails."""
+
+    def __init__(
+        self,
+        message: str = "Memory serialization failed",
+        *,
+        content_type: str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.content_type = content_type
+
+
+class MemoryScopeError(MemoryError):
+    """Raised when memory scope operations fail."""
+
+    def __init__(
+        self,
+        message: str = "Memory scope error",
+        *,
+        requested_scope: str | None = None,
+        allowed_scopes: list[str] | None = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.requested_scope = requested_scope
+        self.allowed_scopes = allowed_scopes or []
+
+
+class MemoryConsolidationError(MemoryError):
+    """Raised when memory consolidation fails."""
+
+    def __init__(
+        self,
+        message: str = "Memory consolidation failed",
+        *,
+        source_type: str | None = None,
+        target_type: str | None = None,
+        items_processed: int = 0,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.source_type = source_type
+        self.target_type = target_type
+        self.items_processed = items_processed
+
+
+class MemoryRetrievalError(MemoryError):
+    """Raised when memory retrieval fails."""
+
+    def __init__(
+        self,
+        message: str = "Memory retrieval failed",
+        *,
+        query: str | None = None,
+        retrieval_type: str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.query = query
+        self.retrieval_type = retrieval_type
+
+
+class EmbeddingError(MemoryError):
+    """Raised when embedding generation fails."""
+
+    def __init__(
+        self,
+        message: str = "Embedding generation failed",
+        *,
+        content_length: int = 0,
+        model: str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.content_length = content_length
+        self.model = model
+
+
+class CheckpointError(MemoryError):
+    """Raised when checkpoint operations fail."""
+
+    def __init__(
+        self,
+        message: str = "Checkpoint operation failed",
+        *,
+        checkpoint_id: str | None = None,
+        operation: str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.checkpoint_id = checkpoint_id
+        self.operation = operation
+
+
+class MemoryConflictError(MemoryError):
+    """Raised when there's a conflict in memory (e.g., contradictory facts)."""
+
+    def __init__(
+        self,
+        message: str = "Memory conflict detected",
+        *,
+        conflicting_ids: list[str | UUID] | None = None,
+        conflict_type: str | None = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(message, **kwargs)
+        self.conflicting_ids = conflicting_ids or []
+        self.conflict_type = conflict_type

backend/app/services/memory/indexing/__init__.py

@@ -0,0 +1,56 @@
+# app/services/memory/indexing/__init__.py
+"""
+Memory Indexing & Retrieval.
+
+Provides vector embeddings and multiple index types for efficient memory search:
+- Vector index for semantic similarity
+- Temporal index for time-based queries
+- Entity index for entity lookups
+- Outcome index for success/failure filtering
+"""
+
+from .index import (
+    EntityIndex,
+    EntityIndexEntry,
+    IndexEntry,
+    MemoryIndex,
+    MemoryIndexer,
+    OutcomeIndex,
+    OutcomeIndexEntry,
+    TemporalIndex,
+    TemporalIndexEntry,
+    VectorIndex,
+    VectorIndexEntry,
+    get_memory_indexer,
+)
+from .retrieval import (
+    CacheEntry,
+    RelevanceScorer,
+    RetrievalCache,
+    RetrievalEngine,
+    RetrievalQuery,
+    ScoredResult,
+    get_retrieval_engine,
+)
+
+__all__ = [
+    "CacheEntry",
+    "EntityIndex",
+    "EntityIndexEntry",
+    "IndexEntry",
+    "MemoryIndex",
+    "MemoryIndexer",
+    "OutcomeIndex",
+    "OutcomeIndexEntry",
+    "RelevanceScorer",
+    "RetrievalCache",
+    "RetrievalEngine",
+    "RetrievalQuery",
+    "ScoredResult",
+    "TemporalIndex",
+    "TemporalIndexEntry",
+    "VectorIndex",
+    "VectorIndexEntry",
+    "get_memory_indexer",
+    "get_retrieval_engine",
+]

backend/app/services/memory/indexing/index.py

@@ -0,0 +1,858 @@
+# app/services/memory/indexing/index.py
+"""
+Memory Indexing.
+
+Provides multiple indexing strategies for efficient memory retrieval:
+- Vector embeddings for semantic search
+- Temporal index for time-based queries
+- Entity index for entity-based lookups
+- Outcome index for success/failure filtering
+"""
+
+import logging
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+from datetime import UTC, datetime, timedelta
+from typing import Any, TypeVar
+from uuid import UUID
+
+from app.services.memory.types import Episode, Fact, MemoryType, Outcome, Procedure
+
+logger = logging.getLogger(__name__)
+
+T = TypeVar("T", Episode, Fact, Procedure)
+
+
+def _utcnow() -> datetime:
+    """Get current UTC time as timezone-aware datetime."""
+    return datetime.now(UTC)
+
+
+@dataclass
+class IndexEntry:
+    """A single entry in an index."""
+
+    memory_id: UUID
+    memory_type: MemoryType
+    indexed_at: datetime = field(default_factory=_utcnow)
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class VectorIndexEntry(IndexEntry):
+    """An entry with vector embedding."""
+
+    embedding: list[float] = field(default_factory=list)
+    dimension: int = 0
+
+    def __post_init__(self) -> None:
+        """Set dimension from embedding."""
+        if self.embedding:
+            self.dimension = len(self.embedding)
+
+
+@dataclass
+class TemporalIndexEntry(IndexEntry):
+    """An entry indexed by time."""
+
+    timestamp: datetime = field(default_factory=_utcnow)
+
+
+@dataclass
+class EntityIndexEntry(IndexEntry):
+    """An entry indexed by entity."""
+
+    entity_type: str = ""
+    entity_value: str = ""
+
+
+@dataclass
+class OutcomeIndexEntry(IndexEntry):
+    """An entry indexed by outcome."""
+
+    outcome: Outcome = Outcome.SUCCESS
+
+
+class MemoryIndex[T](ABC):
+    """Abstract base class for memory indices."""
+
+    @abstractmethod
+    async def add(self, item: T) -> IndexEntry:
+        """Add an item to the index."""
+        ...
+
+    @abstractmethod
+    async def remove(self, memory_id: UUID) -> bool:
+        """Remove an item from the index."""
+        ...
+
+    @abstractmethod
+    async def search(
+        self,
+        query: Any,
+        limit: int = 10,
+        **kwargs: Any,
+    ) -> list[IndexEntry]:
+        """Search the index."""
+        ...
+
+    @abstractmethod
+    async def clear(self) -> int:
+        """Clear all entries from the index."""
+        ...
+
+    @abstractmethod
+    async def count(self) -> int:
+        """Get the number of entries in the index."""
+        ...
+
+
+class VectorIndex(MemoryIndex[T]):
+    """
+    Vector-based index using embeddings for semantic similarity search.
+
+    Uses cosine similarity for matching.
+    """
+
+    def __init__(self, dimension: int = 1536) -> None:
+        """
+        Initialize the vector index.
+
+        Args:
+            dimension: Embedding dimension (default 1536 for OpenAI)
+        """
+        self._dimension = dimension
+        self._entries: dict[UUID, VectorIndexEntry] = {}
+        logger.info(f"Initialized VectorIndex with dimension={dimension}")
+
+    async def add(self, item: T) -> VectorIndexEntry:
+        """
+        Add an item to the vector index.
+
+        Args:
+            item: Memory item with embedding
+
+        Returns:
+            The created index entry
+        """
+        embedding = getattr(item, "embedding", None) or []
+
+        entry = VectorIndexEntry(
+            memory_id=item.id,
+            memory_type=self._get_memory_type(item),
+            embedding=embedding,
+            dimension=len(embedding),
+        )
+
+        self._entries[item.id] = entry
+        logger.debug(f"Added {item.id} to vector index")
+        return entry
+
+    async def remove(self, memory_id: UUID) -> bool:
+        """Remove an item from the vector index."""
+        if memory_id in self._entries:
+            del self._entries[memory_id]
+            logger.debug(f"Removed {memory_id} from vector index")
+            return True
+        return False
+
+    async def search(  # type: ignore[override]
+        self,
+        query: Any,
+        limit: int = 10,
+        min_similarity: float = 0.0,
+        **kwargs: Any,
+    ) -> list[VectorIndexEntry]:
+        """
+        Search for similar items using vector similarity.
+
+        Args:
+            query: Query embedding vector
+            limit: Maximum results to return
+            min_similarity: Minimum similarity threshold (0-1)
+            **kwargs: Additional filter parameters
+
+        Returns:
+            List of matching entries sorted by similarity
+        """
+        if not isinstance(query, list) or not query:
+            return []
+
+        results: list[tuple[float, VectorIndexEntry]] = []
+
+        for entry in self._entries.values():
+            if not entry.embedding:
+                continue
+
+            similarity = self._cosine_similarity(query, entry.embedding)
+            if similarity >= min_similarity:
+                results.append((similarity, entry))
+
+        # Sort by similarity descending
+        results.sort(key=lambda x: x[0], reverse=True)
+
+        # Apply memory type filter if provided
+        memory_type = kwargs.get("memory_type")
+        if memory_type:
+            results = [(s, e) for s, e in results if e.memory_type == memory_type]
+
+        # Store similarity in metadata for the returned entries
+        # Use a copy of metadata to avoid mutating cached entries
+        output = []
+        for similarity, entry in results[:limit]:
+            # Create a shallow copy of the entry with updated metadata
+            entry_with_score = VectorIndexEntry(
+                memory_id=entry.memory_id,
+                memory_type=entry.memory_type,
+                embedding=entry.embedding,
+                metadata={**entry.metadata, "similarity": similarity},
+            )
+            output.append(entry_with_score)
+
+        logger.debug(f"Vector search returned {len(output)} results")
+        return output
+
+    async def clear(self) -> int:
+        """Clear all entries from the index."""
+        count = len(self._entries)
+        self._entries.clear()
+        logger.info(f"Cleared {count} entries from vector index")
+        return count
+
+    async def count(self) -> int:
+        """Get the number of entries in the index."""
+        return len(self._entries)
+
+    def _cosine_similarity(self, a: list[float], b: list[float]) -> float:
+        """Calculate cosine similarity between two vectors."""
+        if len(a) != len(b) or len(a) == 0:
+            return 0.0
+
+        dot_product = sum(x * y for x, y in zip(a, b, strict=True))
+        norm_a = sum(x * x for x in a) ** 0.5
+        norm_b = sum(x * x for x in b) ** 0.5
+
+        if norm_a == 0 or norm_b == 0:
+            return 0.0
+
+        return dot_product / (norm_a * norm_b)
+
+    def _get_memory_type(self, item: T) -> MemoryType:
+        """Get the memory type for an item."""
+        if isinstance(item, Episode):
+            return MemoryType.EPISODIC
+        elif isinstance(item, Fact):
+            return MemoryType.SEMANTIC
+        elif isinstance(item, Procedure):
+            return MemoryType.PROCEDURAL
+        return MemoryType.WORKING
+
+
+class TemporalIndex(MemoryIndex[T]):
+    """
+    Time-based index for efficient temporal queries.
+
+    Supports:
+    - Range queries (between timestamps)
+    - Recent items (within last N seconds/hours/days)
+    - Oldest/newest sorting
+    """
+
+    def __init__(self) -> None:
+        """Initialize the temporal index."""
+        self._entries: dict[UUID, TemporalIndexEntry] = {}
+        # Sorted list for efficient range queries
+        self._sorted_entries: list[tuple[datetime, UUID]] = []
+        logger.info("Initialized TemporalIndex")
+
+    async def add(self, item: T) -> TemporalIndexEntry:
+        """
+        Add an item to the temporal index.
+
+        Args:
+            item: Memory item with timestamp
+
+        Returns:
+            The created index entry
+        """
+        # Get timestamp from various possible fields
+        timestamp = self._get_timestamp(item)
+
+        entry = TemporalIndexEntry(
+            memory_id=item.id,
+            memory_type=self._get_memory_type(item),
+            timestamp=timestamp,
+        )
+
+        self._entries[item.id] = entry
+        self._insert_sorted(timestamp, item.id)
+
+        logger.debug(f"Added {item.id} to temporal index at {timestamp}")
+        return entry
+
+    async def remove(self, memory_id: UUID) -> bool:
+        """Remove an item from the temporal index."""
+        if memory_id not in self._entries:
+            return False
+
+        self._entries.pop(memory_id)
+        self._sorted_entries = [
+            (ts, mid) for ts, mid in self._sorted_entries if mid != memory_id
+        ]
+
+        logger.debug(f"Removed {memory_id} from temporal index")
+        return True
+
+    async def search(  # type: ignore[override]
+        self,
+        query: Any,
+        limit: int = 10,
+        start_time: datetime | None = None,
+        end_time: datetime | None = None,
+        recent_seconds: float | None = None,
+        order: str = "desc",
+        **kwargs: Any,
+    ) -> list[TemporalIndexEntry]:
+        """
+        Search for items by time.
+
+        Args:
+            query: Ignored for temporal search
+            limit: Maximum results to return
+            start_time: Start of time range
+            end_time: End of time range
+            recent_seconds: Get items from last N seconds
+            order: Sort order ("asc" or "desc")
+            **kwargs: Additional filter parameters
+
+        Returns:
+            List of matching entries sorted by time
+        """
+        if recent_seconds is not None:
+            start_time = _utcnow() - timedelta(seconds=recent_seconds)
+            end_time = _utcnow()
+
+        # Filter by time range
+        results: list[TemporalIndexEntry] = []
+        for entry in self._entries.values():
+            if start_time and entry.timestamp < start_time:
+                continue
+            if end_time and entry.timestamp > end_time:
+                continue
+            results.append(entry)
+
+        # Apply memory type filter if provided
+        memory_type = kwargs.get("memory_type")
+        if memory_type:
+            results = [e for e in results if e.memory_type == memory_type]
+
+        # Sort by timestamp
+        results.sort(key=lambda e: e.timestamp, reverse=(order == "desc"))
+
+        logger.debug(f"Temporal search returned {min(len(results), limit)} results")
+        return results[:limit]
+
+    async def clear(self) -> int:
+        """Clear all entries from the index."""
+        count = len(self._entries)
+        self._entries.clear()
+        self._sorted_entries.clear()
+        logger.info(f"Cleared {count} entries from temporal index")
+        return count
+
+    async def count(self) -> int:
+        """Get the number of entries in the index."""
+        return len(self._entries)
+
+    def _insert_sorted(self, timestamp: datetime, memory_id: UUID) -> None:
+        """Insert entry maintaining sorted order."""
+        # Binary search insert for efficiency
+        low, high = 0, len(self._sorted_entries)
+        while low < high:
+            mid = (low + high) // 2
+            if self._sorted_entries[mid][0] < timestamp:
+                low = mid + 1
+            else:
+                high = mid
+        self._sorted_entries.insert(low, (timestamp, memory_id))
+
+    def _get_timestamp(self, item: T) -> datetime:
+        """Get the relevant timestamp for an item."""
+        if hasattr(item, "occurred_at"):
+            return item.occurred_at
+        if hasattr(item, "first_learned"):
+            return item.first_learned
+        if hasattr(item, "last_used") and item.last_used:
+            return item.last_used
+        if hasattr(item, "created_at"):
+            return item.created_at
+        return _utcnow()
+
+    def _get_memory_type(self, item: T) -> MemoryType:
+        """Get the memory type for an item."""
+        if isinstance(item, Episode):
+            return MemoryType.EPISODIC
+        elif isinstance(item, Fact):
+            return MemoryType.SEMANTIC
+        elif isinstance(item, Procedure):
+            return MemoryType.PROCEDURAL
+        return MemoryType.WORKING
+
+
+class EntityIndex(MemoryIndex[T]):
+    """
+    Entity-based index for lookups by entities mentioned in memories.
+
+    Supports:
+    - Single entity lookup
+    - Multi-entity intersection
+    - Entity type filtering
+    """
+
+    def __init__(self) -> None:
+        """Initialize the entity index."""
+        # Main storage
+        self._entries: dict[UUID, EntityIndexEntry] = {}
+        # Inverted index: entity -> set of memory IDs
+        self._entity_to_memories: dict[str, set[UUID]] = {}
+        # Memory to entities mapping
+        self._memory_to_entities: dict[UUID, set[str]] = {}
+        logger.info("Initialized EntityIndex")
+
+    async def add(self, item: T) -> EntityIndexEntry:
+        """
+        Add an item to the entity index.
+
+        Args:
+            item: Memory item with entity information
+
+        Returns:
+            The created index entry
+        """
+        entities = self._extract_entities(item)
+
+        # Create entry for the primary entity (or first one)
+        primary_entity = entities[0] if entities else ("unknown", "unknown")
+
+        entry = EntityIndexEntry(
+            memory_id=item.id,
+            memory_type=self._get_memory_type(item),
+            entity_type=primary_entity[0],
+            entity_value=primary_entity[1],
+        )
+
+        self._entries[item.id] = entry
+
+        # Update inverted indices
+        entity_keys = {f"{etype}:{evalue}" for etype, evalue in entities}
+        self._memory_to_entities[item.id] = entity_keys
+
+        for entity_key in entity_keys:
+            if entity_key not in self._entity_to_memories:
+                self._entity_to_memories[entity_key] = set()
+            self._entity_to_memories[entity_key].add(item.id)
+
+        logger.debug(f"Added {item.id} to entity index with {len(entities)} entities")
+        return entry
+
+    async def remove(self, memory_id: UUID) -> bool:
+        """Remove an item from the entity index."""
+        if memory_id not in self._entries:
+            return False
+
+        # Remove from inverted index
+        if memory_id in self._memory_to_entities:
+            for entity_key in self._memory_to_entities[memory_id]:
+                if entity_key in self._entity_to_memories:
+                    self._entity_to_memories[entity_key].discard(memory_id)
+                    if not self._entity_to_memories[entity_key]:
+                        del self._entity_to_memories[entity_key]
+            del self._memory_to_entities[memory_id]
+
+        del self._entries[memory_id]
+        logger.debug(f"Removed {memory_id} from entity index")
+        return True
+
+    async def search(  # type: ignore[override]
+        self,
+        query: Any,
+        limit: int = 10,
+        entity_type: str | None = None,
+        entity_value: str | None = None,
+        entities: list[tuple[str, str]] | None = None,
+        match_all: bool = False,
+        **kwargs: Any,
+    ) -> list[EntityIndexEntry]:
+        """
+        Search for items by entity.
+
+        Args:
+            query: Entity value to search (if entity_type not specified)
+            limit: Maximum results to return
+            entity_type: Type of entity to filter
+            entity_value: Specific entity value
+            entities: List of (type, value) tuples to match
+            match_all: If True, require all entities to match
+            **kwargs: Additional filter parameters
+
+        Returns:
+            List of matching entries
+        """
+        matching_ids: set[UUID] | None = None
+
+        # Handle single entity query
+        if entity_type and entity_value:
+            entities = [(entity_type, entity_value)]
+        elif entity_value is None and isinstance(query, str):
+            # Search across all entity types
+            entity_value = query
+
+        if entities:
+            for etype, evalue in entities:
+                entity_key = f"{etype}:{evalue}"
+                if entity_key in self._entity_to_memories:
+                    ids = self._entity_to_memories[entity_key]
+                    if matching_ids is None:
+                        matching_ids = ids.copy()
+                    elif match_all:
+                        matching_ids &= ids
+                    else:
+                        matching_ids |= ids
+                elif match_all:
+                    # Required entity not found
+                    matching_ids = set()
+                    break
+        elif entity_value:
+            # Search for value across all types
+            matching_ids = set()
+            for entity_key, ids in self._entity_to_memories.items():
+                if entity_value.lower() in entity_key.lower():
+                    matching_ids |= ids
+
+        if matching_ids is None:
+            matching_ids = set(self._entries.keys())
+
+        # Apply memory type filter if provided
+        memory_type = kwargs.get("memory_type")
+        results = []
+        for mid in matching_ids:
+            if mid in self._entries:
+                entry = self._entries[mid]
+                if memory_type and entry.memory_type != memory_type:
+                    continue
+                results.append(entry)
+
+        logger.debug(f"Entity search returned {min(len(results), limit)} results")
+        return results[:limit]
+
+    async def clear(self) -> int:
+        """Clear all entries from the index."""
+        count = len(self._entries)
+        self._entries.clear()
+        self._entity_to_memories.clear()
+        self._memory_to_entities.clear()
+        logger.info(f"Cleared {count} entries from entity index")
+        return count
+
+    async def count(self) -> int:
+        """Get the number of entries in the index."""
+        return len(self._entries)
+
+    async def get_entities(self, memory_id: UUID) -> list[tuple[str, str]]:
+        """Get all entities for a memory item."""
+        if memory_id not in self._memory_to_entities:
+            return []
+
+        entities = []
+        for entity_key in self._memory_to_entities[memory_id]:
+            if ":" in entity_key:
+                etype, evalue = entity_key.split(":", 1)
+                entities.append((etype, evalue))
+        return entities
+
+    def _extract_entities(self, item: T) -> list[tuple[str, str]]:
+        """Extract entities from a memory item."""
+        entities: list[tuple[str, str]] = []
+
+        if isinstance(item, Episode):
+            # Extract from task type and context
+            entities.append(("task_type", item.task_type))
+            if item.project_id:
+                entities.append(("project", str(item.project_id)))
+            if item.agent_instance_id:
+                entities.append(("agent_instance", str(item.agent_instance_id)))
+            if item.agent_type_id:
+                entities.append(("agent_type", str(item.agent_type_id)))
+
+        elif isinstance(item, Fact):
+            # Subject and object are entities
+            entities.append(("subject", item.subject))
+            entities.append(("object", item.object))
+            if item.project_id:
+                entities.append(("project", str(item.project_id)))
+
+        elif isinstance(item, Procedure):
+            entities.append(("procedure", item.name))
+            if item.project_id:
+                entities.append(("project", str(item.project_id)))
+            if item.agent_type_id:
+                entities.append(("agent_type", str(item.agent_type_id)))
+
+        return entities
+
+    def _get_memory_type(self, item: T) -> MemoryType:
+        """Get the memory type for an item."""
+        if isinstance(item, Episode):
+            return MemoryType.EPISODIC
+        elif isinstance(item, Fact):
+            return MemoryType.SEMANTIC
+        elif isinstance(item, Procedure):
+            return MemoryType.PROCEDURAL
+        return MemoryType.WORKING
+
+
+class OutcomeIndex(MemoryIndex[T]):
+    """
+    Outcome-based index for filtering by success/failure.
+
+    Primarily used for episodes and procedures.
+    """
+
+    def __init__(self) -> None:
+        """Initialize the outcome index."""
+        self._entries: dict[UUID, OutcomeIndexEntry] = {}
+        # Inverted index by outcome
+        self._outcome_to_memories: dict[Outcome, set[UUID]] = {
+            Outcome.SUCCESS: set(),
+            Outcome.FAILURE: set(),
+            Outcome.PARTIAL: set(),
+        }
+        logger.info("Initialized OutcomeIndex")
+
+    async def add(self, item: T) -> OutcomeIndexEntry:
+        """
+        Add an item to the outcome index.
+
+        Args:
+            item: Memory item with outcome information
+
+        Returns:
+            The created index entry
+        """
+        outcome = self._get_outcome(item)
+
+        entry = OutcomeIndexEntry(
+            memory_id=item.id,
+            memory_type=self._get_memory_type(item),
+            outcome=outcome,
+        )
+
+        self._entries[item.id] = entry
+        self._outcome_to_memories[outcome].add(item.id)
+
+        logger.debug(f"Added {item.id} to outcome index with {outcome.value}")
+        return entry
+
+    async def remove(self, memory_id: UUID) -> bool:
+        """Remove an item from the outcome index."""
+        if memory_id not in self._entries:
+            return False
+
+        entry = self._entries.pop(memory_id)
+        self._outcome_to_memories[entry.outcome].discard(memory_id)
+
+        logger.debug(f"Removed {memory_id} from outcome index")
+        return True
+
+    async def search(  # type: ignore[override]
+        self,
+        query: Any,
+        limit: int = 10,
+        outcome: Outcome | None = None,
+        outcomes: list[Outcome] | None = None,
+        **kwargs: Any,
+    ) -> list[OutcomeIndexEntry]:
+        """
+        Search for items by outcome.
+
+        Args:
+            query: Ignored for outcome search
+            limit: Maximum results to return
+            outcome: Single outcome to filter
+            outcomes: Multiple outcomes to filter (OR)
+            **kwargs: Additional filter parameters
+
+        Returns:
+            List of matching entries
+        """
+        if outcome:
+            outcomes = [outcome]
+
+        if outcomes:
+            matching_ids: set[UUID] = set()
+            for o in outcomes:
+                matching_ids |= self._outcome_to_memories.get(o, set())
+        else:
+            matching_ids = set(self._entries.keys())
+
+        # Apply memory type filter if provided
+        memory_type = kwargs.get("memory_type")
+        results = []
+        for mid in matching_ids:
+            if mid in self._entries:
+                entry = self._entries[mid]
+                if memory_type and entry.memory_type != memory_type:
+                    continue
+                results.append(entry)
+
+        logger.debug(f"Outcome search returned {min(len(results), limit)} results")
+        return results[:limit]
+
+    async def clear(self) -> int:
+        """Clear all entries from the index."""
+        count = len(self._entries)
+        self._entries.clear()
+        for outcome in self._outcome_to_memories:
+            self._outcome_to_memories[outcome].clear()
+        logger.info(f"Cleared {count} entries from outcome index")
+        return count
+
+    async def count(self) -> int:
+        """Get the number of entries in the index."""
+        return len(self._entries)
+
+    async def get_outcome_stats(self) -> dict[Outcome, int]:
+        """Get statistics on outcomes."""
+        return {outcome: len(ids) for outcome, ids in self._outcome_to_memories.items()}
+
+    def _get_outcome(self, item: T) -> Outcome:
+        """Get the outcome for an item."""
+        if isinstance(item, Episode):
+            return item.outcome
+        elif isinstance(item, Procedure):
+            # Derive from success rate
+            if item.success_rate >= 0.8:
+                return Outcome.SUCCESS
+            elif item.success_rate <= 0.2:
+                return Outcome.FAILURE
+            return Outcome.PARTIAL
+        return Outcome.SUCCESS
+
+    def _get_memory_type(self, item: T) -> MemoryType:
+        """Get the memory type for an item."""
+        if isinstance(item, Episode):
+            return MemoryType.EPISODIC
+        elif isinstance(item, Fact):
+            return MemoryType.SEMANTIC
+        elif isinstance(item, Procedure):
+            return MemoryType.PROCEDURAL
+        return MemoryType.WORKING
+
+
+@dataclass
+class MemoryIndexer:
+    """
+    Unified indexer that manages all index types.
+
+    Provides a single interface for indexing and searching across
+    multiple index types.
+    """
+
+    vector_index: VectorIndex[Any] = field(default_factory=VectorIndex)
+    temporal_index: TemporalIndex[Any] = field(default_factory=TemporalIndex)
+    entity_index: EntityIndex[Any] = field(default_factory=EntityIndex)
+    outcome_index: OutcomeIndex[Any] = field(default_factory=OutcomeIndex)
+
+    async def index(self, item: Episode | Fact | Procedure) -> dict[str, IndexEntry]:
+        """
+        Index an item across all applicable indices.
+
+        Args:
+            item: Memory item to index
+
+        Returns:
+            Dictionary of index type to entry
+        """
+        results: dict[str, IndexEntry] = {}
+
+        # Vector index (if embedding present)
+        if getattr(item, "embedding", None):
+            results["vector"] = await self.vector_index.add(item)
+
+        # Temporal index
+        results["temporal"] = await self.temporal_index.add(item)
+
+        # Entity index
+        results["entity"] = await self.entity_index.add(item)
+
+        # Outcome index (for episodes and procedures)
+        if isinstance(item, (Episode, Procedure)):
+            results["outcome"] = await self.outcome_index.add(item)
+
+        logger.info(
+            f"Indexed {item.id} across {len(results)} indices: {list(results.keys())}"
+        )
+        return results
+
+    async def remove(self, memory_id: UUID) -> dict[str, bool]:
+        """
+        Remove an item from all indices.
+
+        Args:
+            memory_id: ID of the memory to remove
+
+        Returns:
+            Dictionary of index type to removal success
+        """
+        results = {
+            "vector": await self.vector_index.remove(memory_id),
+            "temporal": await self.temporal_index.remove(memory_id),
+            "entity": await self.entity_index.remove(memory_id),
+            "outcome": await self.outcome_index.remove(memory_id),
+        }
+
+        removed_from = [k for k, v in results.items() if v]
+        if removed_from:
+            logger.info(f"Removed {memory_id} from indices: {removed_from}")
+
+        return results
+
+    async def clear_all(self) -> dict[str, int]:
+        """
+        Clear all indices.
+
+        Returns:
+            Dictionary of index type to count cleared
+        """
+        return {
+            "vector": await self.vector_index.clear(),
+            "temporal": await self.temporal_index.clear(),
+            "entity": await self.entity_index.clear(),
+            "outcome": await self.outcome_index.clear(),
+        }
+
+    async def get_stats(self) -> dict[str, int]:
+        """
+        Get statistics for all indices.
+
+        Returns:
+            Dictionary of index type to entry count
+        """
+        return {
+            "vector": await self.vector_index.count(),
+            "temporal": await self.temporal_index.count(),
+            "entity": await self.entity_index.count(),
+            "outcome": await self.outcome_index.count(),
+        }
+
+
+# Singleton indexer instance
+_indexer: MemoryIndexer | None = None
+
+
+def get_memory_indexer() -> MemoryIndexer:
+    """Get the singleton memory indexer instance."""
+    global _indexer
+    if _indexer is None:
+        _indexer = MemoryIndexer()
+    return _indexer

backend/app/services/memory/indexing/retrieval.py

@@ -0,0 +1,742 @@
+# app/services/memory/indexing/retrieval.py
+"""
+Memory Retrieval Engine.
+
+Provides hybrid retrieval capabilities combining:
+- Vector similarity search
+- Temporal filtering
+- Entity filtering
+- Outcome filtering
+- Relevance scoring
+- Result caching
+"""
+
+import hashlib
+import logging
+from collections import OrderedDict
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from typing import Any, TypeVar
+from uuid import UUID
+
+from app.services.memory.types import (
+    Episode,
+    Fact,
+    MemoryType,
+    Outcome,
+    Procedure,
+    RetrievalResult,
+)
+
+from .index import (
+    MemoryIndexer,
+    get_memory_indexer,
+)
+
+logger = logging.getLogger(__name__)
+
+T = TypeVar("T", Episode, Fact, Procedure)
+
+
+def _utcnow() -> datetime:
+    """Get current UTC time as timezone-aware datetime."""
+    return datetime.now(UTC)
+
+
+@dataclass
+class RetrievalQuery:
+    """Query parameters for memory retrieval."""
+
+    # Text/semantic query
+    query_text: str | None = None
+    query_embedding: list[float] | None = None
+
+    # Temporal filters
+    start_time: datetime | None = None
+    end_time: datetime | None = None
+    recent_seconds: float | None = None
+
+    # Entity filters
+    entities: list[tuple[str, str]] | None = None
+    entity_match_all: bool = False
+
+    # Outcome filters
+    outcomes: list[Outcome] | None = None
+
+    # Memory type filter
+    memory_types: list[MemoryType] | None = None
+
+    # Result options
+    limit: int = 10
+    min_relevance: float = 0.0
+
+    # Retrieval mode
+    use_vector: bool = True
+    use_temporal: bool = True
+    use_entity: bool = True
+    use_outcome: bool = True
+
+    def to_cache_key(self) -> str:
+        """Generate a cache key for this query."""
+        key_parts = [
+            self.query_text or "",
+            str(self.start_time),
+            str(self.end_time),
+            str(self.recent_seconds),
+            str(self.entities),
+            str(self.outcomes),
+            str(self.memory_types),
+            str(self.limit),
+            str(self.min_relevance),
+        ]
+        key_string = "|".join(key_parts)
+        return hashlib.sha256(key_string.encode()).hexdigest()[:32]
+
+
+@dataclass
+class ScoredResult:
+    """A retrieval result with relevance score."""
+
+    memory_id: UUID
+    memory_type: MemoryType
+    relevance_score: float
+    score_breakdown: dict[str, float] = field(default_factory=dict)
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class CacheEntry:
+    """A cached retrieval result."""
+
+    results: list[ScoredResult]
+    created_at: datetime
+    ttl_seconds: float
+    query_key: str
+
+    def is_expired(self) -> bool:
+        """Check if this cache entry has expired."""
+        age = (_utcnow() - self.created_at).total_seconds()
+        return age > self.ttl_seconds
+
+
+class RelevanceScorer:
+    """
+    Calculates relevance scores for retrieved memories.
+
+    Combines multiple signals:
+    - Vector similarity (if available)
+    - Temporal recency
+    - Entity match count
+    - Outcome preference
+    - Importance/confidence
+    """
+
+    def __init__(
+        self,
+        vector_weight: float = 0.4,
+        recency_weight: float = 0.2,
+        entity_weight: float = 0.2,
+        outcome_weight: float = 0.1,
+        importance_weight: float = 0.1,
+    ) -> None:
+        """
+        Initialize the relevance scorer.
+
+        Args:
+            vector_weight: Weight for vector similarity (0-1)
+            recency_weight: Weight for temporal recency (0-1)
+            entity_weight: Weight for entity matches (0-1)
+            outcome_weight: Weight for outcome preference (0-1)
+            importance_weight: Weight for importance score (0-1)
+        """
+        total = (
+            vector_weight
+            + recency_weight
+            + entity_weight
+            + outcome_weight
+            + importance_weight
+        )
+        # Normalize weights
+        self.vector_weight = vector_weight / total
+        self.recency_weight = recency_weight / total
+        self.entity_weight = entity_weight / total
+        self.outcome_weight = outcome_weight / total
+        self.importance_weight = importance_weight / total
+
+    def score(
+        self,
+        memory_id: UUID,
+        memory_type: MemoryType,
+        vector_similarity: float | None = None,
+        timestamp: datetime | None = None,
+        entity_match_count: int = 0,
+        entity_total: int = 1,
+        outcome: Outcome | None = None,
+        importance: float = 0.5,
+        preferred_outcomes: list[Outcome] | None = None,
+    ) -> ScoredResult:
+        """
+        Calculate a relevance score for a memory.
+
+        Args:
+            memory_id: ID of the memory
+            memory_type: Type of memory
+            vector_similarity: Similarity score from vector search (0-1)
+            timestamp: Timestamp of the memory
+            entity_match_count: Number of matching entities
+            entity_total: Total entities in query
+            outcome: Outcome of the memory
+            importance: Importance score of the memory (0-1)
+            preferred_outcomes: Outcomes to prefer
+
+        Returns:
+            Scored result with breakdown
+        """
+        breakdown: dict[str, float] = {}
+
+        # Vector similarity score
+        if vector_similarity is not None:
+            breakdown["vector"] = vector_similarity
+        else:
+            breakdown["vector"] = 0.5  # Neutral if no vector
+
+        # Recency score (exponential decay)
+        if timestamp:
+            age_hours = (_utcnow() - timestamp).total_seconds() / 3600
+            # Decay with half-life of 24 hours
+            breakdown["recency"] = 2 ** (-age_hours / 24)
+        else:
+            breakdown["recency"] = 0.5
+
+        # Entity match score
+        if entity_total > 0:
+            breakdown["entity"] = entity_match_count / entity_total
+        else:
+            breakdown["entity"] = 1.0  # No entity filter = full score
+
+        # Outcome score
+        if preferred_outcomes and outcome:
+            breakdown["outcome"] = 1.0 if outcome in preferred_outcomes else 0.0
+        else:
+            breakdown["outcome"] = 0.5  # Neutral if no preference
+
+        # Importance score
+        breakdown["importance"] = importance
+
+        # Calculate weighted sum
+        total_score = (
+            breakdown["vector"] * self.vector_weight
+            + breakdown["recency"] * self.recency_weight
+            + breakdown["entity"] * self.entity_weight
+            + breakdown["outcome"] * self.outcome_weight
+            + breakdown["importance"] * self.importance_weight
+        )
+
+        return ScoredResult(
+            memory_id=memory_id,
+            memory_type=memory_type,
+            relevance_score=total_score,
+            score_breakdown=breakdown,
+        )
+
+
+class RetrievalCache:
+    """
+    In-memory cache for retrieval results.
+
+    Supports TTL-based expiration and LRU eviction with O(1) operations.
+    Uses OrderedDict for efficient LRU tracking.
+    """
+
+    def __init__(
+        self,
+        max_entries: int = 1000,
+        default_ttl_seconds: float = 300,
+    ) -> None:
+        """
+        Initialize the cache.
+
+        Args:
+            max_entries: Maximum cache entries
+            default_ttl_seconds: Default TTL for entries
+        """
+        # OrderedDict maintains insertion order; we use move_to_end for O(1) LRU
+        self._cache: OrderedDict[str, CacheEntry] = OrderedDict()
+        self._max_entries = max_entries
+        self._default_ttl = default_ttl_seconds
+        logger.info(
+            f"Initialized RetrievalCache with max_entries={max_entries}, "
+            f"ttl={default_ttl_seconds}s"
+        )
+
+    def get(self, query_key: str) -> list[ScoredResult] | None:
+        """
+        Get cached results for a query.
+
+        Args:
+            query_key: Cache key for the query
+
+        Returns:
+            Cached results or None if not found/expired
+        """
+        if query_key not in self._cache:
+            return None
+
+        entry = self._cache[query_key]
+        if entry.is_expired():
+            del self._cache[query_key]
+            return None
+
+        # Update access order (LRU) - O(1) with OrderedDict
+        self._cache.move_to_end(query_key)
+
+        logger.debug(f"Cache hit for {query_key}")
+        return entry.results
+
+    def put(
+        self,
+        query_key: str,
+        results: list[ScoredResult],
+        ttl_seconds: float | None = None,
+    ) -> None:
+        """
+        Cache results for a query.
+
+        Args:
+            query_key: Cache key for the query
+            results: Results to cache
+            ttl_seconds: TTL for this entry (or default)
+        """
+        # Evict oldest entries if at capacity - O(1) with popitem(last=False)
+        while len(self._cache) >= self._max_entries:
+            self._cache.popitem(last=False)
+
+        entry = CacheEntry(
+            results=results,
+            created_at=_utcnow(),
+            ttl_seconds=ttl_seconds or self._default_ttl,
+            query_key=query_key,
+        )
+
+        self._cache[query_key] = entry
+        logger.debug(f"Cached {len(results)} results for {query_key}")
+
+    def invalidate(self, query_key: str) -> bool:
+        """
+        Invalidate a specific cache entry.
+
+        Args:
+            query_key: Cache key to invalidate
+
+        Returns:
+            True if entry was found and removed
+        """
+        if query_key in self._cache:
+            del self._cache[query_key]
+            return True
+        return False
+
+    def invalidate_by_memory(self, memory_id: UUID) -> int:
+        """
+        Invalidate all cache entries containing a specific memory.
+
+        Args:
+            memory_id: Memory ID to invalidate
+
+        Returns:
+            Number of entries invalidated
+        """
+        keys_to_remove = []
+        for key, entry in self._cache.items():
+            if any(r.memory_id == memory_id for r in entry.results):
+                keys_to_remove.append(key)
+
+        for key in keys_to_remove:
+            self.invalidate(key)
+
+        if keys_to_remove:
+            logger.debug(
+                f"Invalidated {len(keys_to_remove)} cache entries for {memory_id}"
+            )
+        return len(keys_to_remove)
+
+    def clear(self) -> int:
+        """
+        Clear all cache entries.
+
+        Returns:
+            Number of entries cleared
+        """
+        count = len(self._cache)
+        self._cache.clear()
+        logger.info(f"Cleared {count} cache entries")
+        return count
+
+    def get_stats(self) -> dict[str, Any]:
+        """Get cache statistics."""
+        expired_count = sum(1 for e in self._cache.values() if e.is_expired())
+        return {
+            "total_entries": len(self._cache),
+            "expired_entries": expired_count,
+            "max_entries": self._max_entries,
+            "default_ttl_seconds": self._default_ttl,
+        }
+
+
+class RetrievalEngine:
+    """
+    Hybrid retrieval engine for memory search.
+
+    Combines multiple index types for comprehensive retrieval:
+    - Vector search for semantic similarity
+    - Temporal index for time-based filtering
+    - Entity index for entity-based lookups
+    - Outcome index for success/failure filtering
+
+    Results are scored and ranked using relevance scoring.
+    """
+
+    def __init__(
+        self,
+        indexer: MemoryIndexer | None = None,
+        scorer: RelevanceScorer | None = None,
+        cache: RetrievalCache | None = None,
+        enable_cache: bool = True,
+    ) -> None:
+        """
+        Initialize the retrieval engine.
+
+        Args:
+            indexer: Memory indexer (defaults to singleton)
+            scorer: Relevance scorer (defaults to new instance)
+            cache: Retrieval cache (defaults to new instance)
+            enable_cache: Whether to enable result caching
+        """
+        self._indexer = indexer or get_memory_indexer()
+        self._scorer = scorer or RelevanceScorer()
+        self._cache = cache or RetrievalCache() if enable_cache else None
+        self._enable_cache = enable_cache
+        logger.info(f"Initialized RetrievalEngine with cache={enable_cache}")
+
+    async def retrieve(
+        self,
+        query: RetrievalQuery,
+        use_cache: bool = True,
+    ) -> RetrievalResult[ScoredResult]:
+        """
+        Retrieve relevant memories using hybrid search.
+
+        Args:
+            query: Retrieval query parameters
+            use_cache: Whether to use cached results
+
+        Returns:
+            Retrieval result with scored items
+        """
+        start_time = _utcnow()
+
+        # Check cache
+        cache_key = query.to_cache_key()
+        if use_cache and self._cache:
+            cached = self._cache.get(cache_key)
+            if cached:
+                latency = (_utcnow() - start_time).total_seconds() * 1000
+                return RetrievalResult(
+                    items=cached,
+                    total_count=len(cached),
+                    query=query.query_text or "",
+                    retrieval_type="cached",
+                    latency_ms=latency,
+                    metadata={"cache_hit": True},
+                )
+
+        # Collect candidates from each index
+        candidates: dict[UUID, dict[str, Any]] = {}
+
+        # Vector search
+        if query.use_vector and query.query_embedding:
+            vector_results = await self._indexer.vector_index.search(
+                query=query.query_embedding,
+                limit=query.limit * 3,  # Get more for filtering
+                min_similarity=query.min_relevance,
+                memory_type=query.memory_types[0] if query.memory_types else None,
+            )
+            for entry in vector_results:
+                if entry.memory_id not in candidates:
+                    candidates[entry.memory_id] = {
+                        "memory_type": entry.memory_type,
+                        "sources": [],
+                    }
+                candidates[entry.memory_id]["vector_similarity"] = entry.metadata.get(
+                    "similarity", 0.5
+                )
+                candidates[entry.memory_id]["sources"].append("vector")
+
+        # Temporal search
+        if query.use_temporal and (
+            query.start_time or query.end_time or query.recent_seconds
+        ):
+            temporal_results = await self._indexer.temporal_index.search(
+                query=None,
+                limit=query.limit * 3,
+                start_time=query.start_time,
+                end_time=query.end_time,
+                recent_seconds=query.recent_seconds,
+                memory_type=query.memory_types[0] if query.memory_types else None,
+            )
+            for temporal_entry in temporal_results:
+                if temporal_entry.memory_id not in candidates:
+                    candidates[temporal_entry.memory_id] = {
+                        "memory_type": temporal_entry.memory_type,
+                        "sources": [],
+                    }
+                candidates[temporal_entry.memory_id]["timestamp"] = (
+                    temporal_entry.timestamp
+                )
+                candidates[temporal_entry.memory_id]["sources"].append("temporal")
+
+        # Entity search
+        if query.use_entity and query.entities:
+            entity_results = await self._indexer.entity_index.search(
+                query=None,
+                limit=query.limit * 3,
+                entities=query.entities,
+                match_all=query.entity_match_all,
+                memory_type=query.memory_types[0] if query.memory_types else None,
+            )
+            for entity_entry in entity_results:
+                if entity_entry.memory_id not in candidates:
+                    candidates[entity_entry.memory_id] = {
+                        "memory_type": entity_entry.memory_type,
+                        "sources": [],
+                    }
+                # Count entity matches
+                entity_count = candidates[entity_entry.memory_id].get(
+                    "entity_match_count", 0
+                )
+                candidates[entity_entry.memory_id]["entity_match_count"] = (
+                    entity_count + 1
+                )
+                candidates[entity_entry.memory_id]["sources"].append("entity")
+
+        # Outcome search
+        if query.use_outcome and query.outcomes:
+            outcome_results = await self._indexer.outcome_index.search(
+                query=None,
+                limit=query.limit * 3,
+                outcomes=query.outcomes,
+                memory_type=query.memory_types[0] if query.memory_types else None,
+            )
+            for outcome_entry in outcome_results:
+                if outcome_entry.memory_id not in candidates:
+                    candidates[outcome_entry.memory_id] = {
+                        "memory_type": outcome_entry.memory_type,
+                        "sources": [],
+                    }
+                candidates[outcome_entry.memory_id]["outcome"] = outcome_entry.outcome
+                candidates[outcome_entry.memory_id]["sources"].append("outcome")
+
+        # Score and rank candidates
+        scored_results: list[ScoredResult] = []
+        entity_total = len(query.entities) if query.entities else 1
+
+        for memory_id, data in candidates.items():
+            scored = self._scorer.score(
+                memory_id=memory_id,
+                memory_type=data["memory_type"],
+                vector_similarity=data.get("vector_similarity"),
+                timestamp=data.get("timestamp"),
+                entity_match_count=data.get("entity_match_count", 0),
+                entity_total=entity_total,
+                outcome=data.get("outcome"),
+                preferred_outcomes=query.outcomes,
+            )
+            scored.metadata["sources"] = data.get("sources", [])
+
+            # Filter by minimum relevance
+            if scored.relevance_score >= query.min_relevance:
+                scored_results.append(scored)
+
+        # Sort by relevance score
+        scored_results.sort(key=lambda x: x.relevance_score, reverse=True)
+
+        # Apply limit
+        final_results = scored_results[: query.limit]
+
+        # Cache results
+        if use_cache and self._cache and final_results:
+            self._cache.put(cache_key, final_results)
+
+        latency = (_utcnow() - start_time).total_seconds() * 1000
+
+        logger.info(
+            f"Retrieved {len(final_results)} results from {len(candidates)} candidates "
+            f"in {latency:.2f}ms"
+        )
+
+        return RetrievalResult(
+            items=final_results,
+            total_count=len(candidates),
+            query=query.query_text or "",
+            retrieval_type="hybrid",
+            latency_ms=latency,
+            metadata={
+                "cache_hit": False,
+                "candidates_count": len(candidates),
+                "filtered_count": len(scored_results),
+            },
+        )
+
+    async def retrieve_similar(
+        self,
+        embedding: list[float],
+        limit: int = 10,
+        min_similarity: float = 0.5,
+        memory_types: list[MemoryType] | None = None,
+    ) -> RetrievalResult[ScoredResult]:
+        """
+        Retrieve memories similar to a given embedding.
+
+        Args:
+            embedding: Query embedding
+            limit: Maximum results
+            min_similarity: Minimum similarity threshold
+            memory_types: Filter by memory types
+
+        Returns:
+            Retrieval result with scored items
+        """
+        query = RetrievalQuery(
+            query_embedding=embedding,
+            limit=limit,
+            min_relevance=min_similarity,
+            memory_types=memory_types,
+            use_temporal=False,
+            use_entity=False,
+            use_outcome=False,
+        )
+        return await self.retrieve(query)
+
+    async def retrieve_recent(
+        self,
+        hours: float = 24,
+        limit: int = 10,
+        memory_types: list[MemoryType] | None = None,
+    ) -> RetrievalResult[ScoredResult]:
+        """
+        Retrieve recent memories.
+
+        Args:
+            hours: Number of hours to look back
+            limit: Maximum results
+            memory_types: Filter by memory types
+
+        Returns:
+            Retrieval result with scored items
+        """
+        query = RetrievalQuery(
+            recent_seconds=hours * 3600,
+            limit=limit,
+            memory_types=memory_types,
+            use_vector=False,
+            use_entity=False,
+            use_outcome=False,
+        )
+        return await self.retrieve(query)
+
+    async def retrieve_by_entity(
+        self,
+        entity_type: str,
+        entity_value: str,
+        limit: int = 10,
+        memory_types: list[MemoryType] | None = None,
+    ) -> RetrievalResult[ScoredResult]:
+        """
+        Retrieve memories by entity.
+
+        Args:
+            entity_type: Type of entity
+            entity_value: Entity value
+            limit: Maximum results
+            memory_types: Filter by memory types
+
+        Returns:
+            Retrieval result with scored items
+        """
+        query = RetrievalQuery(
+            entities=[(entity_type, entity_value)],
+            limit=limit,
+            memory_types=memory_types,
+            use_vector=False,
+            use_temporal=False,
+            use_outcome=False,
+        )
+        return await self.retrieve(query)
+
+    async def retrieve_successful(
+        self,
+        limit: int = 10,
+        memory_types: list[MemoryType] | None = None,
+    ) -> RetrievalResult[ScoredResult]:
+        """
+        Retrieve successful memories.
+
+        Args:
+            limit: Maximum results
+            memory_types: Filter by memory types
+
+        Returns:
+            Retrieval result with scored items
+        """
+        query = RetrievalQuery(
+            outcomes=[Outcome.SUCCESS],
+            limit=limit,
+            memory_types=memory_types,
+            use_vector=False,
+            use_temporal=False,
+            use_entity=False,
+        )
+        return await self.retrieve(query)
+
+    def invalidate_cache(self) -> int:
+        """
+        Invalidate all cached results.
+
+        Returns:
+            Number of entries invalidated
+        """
+        if self._cache:
+            return self._cache.clear()
+        return 0
+
+    def invalidate_cache_for_memory(self, memory_id: UUID) -> int:
+        """
+        Invalidate cache entries containing a specific memory.
+
+        Args:
+            memory_id: Memory ID to invalidate
+
+        Returns:
+            Number of entries invalidated
+        """
+        if self._cache:
+            return self._cache.invalidate_by_memory(memory_id)
+        return 0
+
+    def get_cache_stats(self) -> dict[str, Any]:
+        """Get cache statistics."""
+        if self._cache:
+            return self._cache.get_stats()
+        return {"enabled": False}
+
+
+# Singleton retrieval engine instance
+_engine: RetrievalEngine | None = None
+
+
+def get_retrieval_engine() -> RetrievalEngine:
+    """Get the singleton retrieval engine instance."""
+    global _engine
+    if _engine is None:
+        _engine = RetrievalEngine()
+    return _engine

backend/app/services/memory/integration/__init__.py

@@ -0,0 +1,19 @@
+# app/services/memory/integration/__init__.py
+"""
+Memory Integration Module.
+
+Provides integration between the agent memory system and other Syndarix components:
+- Context Engine: Memory as context source
+- Agent Lifecycle: Spawn, pause, resume, terminate hooks
+"""
+
+from .context_source import MemoryContextSource, get_memory_context_source
+from .lifecycle import AgentLifecycleManager, LifecycleHooks, get_lifecycle_manager
+
+__all__ = [
+    "AgentLifecycleManager",
+    "LifecycleHooks",
+    "MemoryContextSource",
+    "get_lifecycle_manager",
+    "get_memory_context_source",
+]

backend/app/services/memory/integration/context_source.py

@@ -0,0 +1,399 @@
+# app/services/memory/integration/context_source.py
+"""
+Memory Context Source.
+
+Provides agent memory as a context source for the Context Engine.
+Retrieves relevant memories based on query and converts them to MemoryContext objects.
+"""
+
+import logging
+from dataclasses import dataclass
+from datetime import UTC, datetime, timedelta
+from typing import Any
+from uuid import UUID
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.services.context.types.memory import MemoryContext
+from app.services.memory.episodic import EpisodicMemory
+from app.services.memory.procedural import ProceduralMemory
+from app.services.memory.semantic import SemanticMemory
+from app.services.memory.working import WorkingMemory
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class MemoryFetchConfig:
+    """Configuration for memory fetching."""
+
+    # Limits per memory type
+    working_limit: int = 10
+    episodic_limit: int = 10
+    semantic_limit: int = 15
+    procedural_limit: int = 5
+
+    # Time ranges
+    episodic_days_back: int = 30
+    min_relevance: float = 0.3
+
+    # Which memory types to include
+    include_working: bool = True
+    include_episodic: bool = True
+    include_semantic: bool = True
+    include_procedural: bool = True
+
+
+@dataclass
+class MemoryFetchResult:
+    """Result of memory fetch operation."""
+
+    contexts: list[MemoryContext]
+    by_type: dict[str, int]
+    fetch_time_ms: float
+    query: str
+
+
+class MemoryContextSource:
+    """
+    Source for memory context in the Context Engine.
+
+    This service retrieves relevant memories based on a query and
+    converts them to MemoryContext objects for context assembly.
+    It coordinates between all memory types (working, episodic,
+    semantic, procedural) to provide a comprehensive memory context.
+    """
+
+    def __init__(
+        self,
+        session: AsyncSession,
+        embedding_generator: Any | None = None,
+    ) -> None:
+        """
+        Initialize the memory context source.
+
+        Args:
+            session: Database session
+            embedding_generator: Optional embedding generator for semantic search
+        """
+        self._session = session
+        self._embedding_generator = embedding_generator
+
+        # Lazy-initialized memory services
+        self._episodic: EpisodicMemory | None = None
+        self._semantic: SemanticMemory | None = None
+        self._procedural: ProceduralMemory | None = None
+
+    async def _get_episodic(self) -> EpisodicMemory:
+        """Get or create episodic memory service."""
+        if self._episodic is None:
+            self._episodic = await EpisodicMemory.create(
+                self._session,
+                self._embedding_generator,
+            )
+        return self._episodic
+
+    async def _get_semantic(self) -> SemanticMemory:
+        """Get or create semantic memory service."""
+        if self._semantic is None:
+            self._semantic = await SemanticMemory.create(
+                self._session,
+                self._embedding_generator,
+            )
+        return self._semantic
+
+    async def _get_procedural(self) -> ProceduralMemory:
+        """Get or create procedural memory service."""
+        if self._procedural is None:
+            self._procedural = await ProceduralMemory.create(
+                self._session,
+                self._embedding_generator,
+            )
+        return self._procedural
+
+    async def fetch_context(
+        self,
+        query: str,
+        project_id: UUID,
+        agent_instance_id: UUID | None = None,
+        agent_type_id: UUID | None = None,
+        session_id: str | None = None,
+        config: MemoryFetchConfig | None = None,
+    ) -> MemoryFetchResult:
+        """
+        Fetch relevant memories as context.
+
+        This is the main entry point for the Context Engine integration.
+        It searches across all memory types and returns relevant memories
+        as MemoryContext objects.
+
+        Args:
+            query: Search query for finding relevant memories
+            project_id: Project scope
+            agent_instance_id: Optional agent instance scope
+            agent_type_id: Optional agent type scope (for procedural)
+            session_id: Optional session ID (for working memory)
+            config: Optional fetch configuration
+
+        Returns:
+            MemoryFetchResult with contexts and metadata
+        """
+        config = config or MemoryFetchConfig()
+        start_time = datetime.now(UTC)
+
+        contexts: list[MemoryContext] = []
+        by_type: dict[str, int] = {
+            "working": 0,
+            "episodic": 0,
+            "semantic": 0,
+            "procedural": 0,
+        }
+
+        # Fetch from working memory (session-scoped)
+        if config.include_working and session_id:
+            try:
+                working_contexts = await self._fetch_working(
+                    query=query,
+                    session_id=session_id,
+                    project_id=project_id,
+                    agent_instance_id=agent_instance_id,
+                    limit=config.working_limit,
+                )
+                contexts.extend(working_contexts)
+                by_type["working"] = len(working_contexts)
+            except Exception as e:
+                logger.warning(f"Failed to fetch working memory: {e}")
+
+        # Fetch from episodic memory
+        if config.include_episodic:
+            try:
+                episodic_contexts = await self._fetch_episodic(
+                    query=query,
+                    project_id=project_id,
+                    agent_instance_id=agent_instance_id,
+                    limit=config.episodic_limit,
+                    days_back=config.episodic_days_back,
+                )
+                contexts.extend(episodic_contexts)
+                by_type["episodic"] = len(episodic_contexts)
+            except Exception as e:
+                logger.warning(f"Failed to fetch episodic memory: {e}")
+
+        # Fetch from semantic memory
+        if config.include_semantic:
+            try:
+                semantic_contexts = await self._fetch_semantic(
+                    query=query,
+                    project_id=project_id,
+                    limit=config.semantic_limit,
+                    min_relevance=config.min_relevance,
+                )
+                contexts.extend(semantic_contexts)
+                by_type["semantic"] = len(semantic_contexts)
+            except Exception as e:
+                logger.warning(f"Failed to fetch semantic memory: {e}")
+
+        # Fetch from procedural memory
+        if config.include_procedural:
+            try:
+                procedural_contexts = await self._fetch_procedural(
+                    query=query,
+                    project_id=project_id,
+                    agent_type_id=agent_type_id,
+                    limit=config.procedural_limit,
+                )
+                contexts.extend(procedural_contexts)
+                by_type["procedural"] = len(procedural_contexts)
+            except Exception as e:
+                logger.warning(f"Failed to fetch procedural memory: {e}")
+
+        # Sort by relevance
+        contexts.sort(key=lambda c: c.relevance_score, reverse=True)
+
+        fetch_time = (datetime.now(UTC) - start_time).total_seconds() * 1000
+
+        logger.debug(
+            f"Fetched {len(contexts)} memory contexts for query '{query[:50]}...' "
+            f"in {fetch_time:.1f}ms"
+        )
+
+        return MemoryFetchResult(
+            contexts=contexts,
+            by_type=by_type,
+            fetch_time_ms=fetch_time,
+            query=query,
+        )
+
+    async def _fetch_working(
+        self,
+        query: str,
+        session_id: str,
+        project_id: UUID,
+        agent_instance_id: UUID | None,
+        limit: int,
+    ) -> list[MemoryContext]:
+        """Fetch from working memory."""
+        working = await WorkingMemory.for_session(
+            session_id=session_id,
+            project_id=str(project_id),
+            agent_instance_id=str(agent_instance_id) if agent_instance_id else None,
+        )
+
+        contexts: list[MemoryContext] = []
+        all_keys = await working.list_keys()
+
+        # Filter keys by query (simple substring match)
+        query_lower = query.lower()
+        matched_keys = [k for k in all_keys if query_lower in k.lower()]
+
+        # If no query match, include all keys (working memory is always relevant)
+        if not matched_keys and query:
+            matched_keys = all_keys
+
+        for key in matched_keys[:limit]:
+            value = await working.get(key)
+            if value is not None:
+                contexts.append(
+                    MemoryContext.from_working_memory(
+                        key=key,
+                        value=value,
+                        source=f"working:{session_id}",
+                        query=query,
+                    )
+                )
+
+        return contexts
+
+    async def _fetch_episodic(
+        self,
+        query: str,
+        project_id: UUID,
+        agent_instance_id: UUID | None,
+        limit: int,
+        days_back: int,
+    ) -> list[MemoryContext]:
+        """Fetch from episodic memory."""
+        episodic = await self._get_episodic()
+
+        # Search for similar episodes
+        episodes = await episodic.search_similar(
+            project_id=project_id,
+            query=query,
+            limit=limit,
+            agent_instance_id=agent_instance_id,
+        )
+
+        # Also get recent episodes if we didn't find enough
+        if len(episodes) < limit // 2:
+            since = datetime.now(UTC) - timedelta(days=days_back)
+            recent = await episodic.get_recent(
+                project_id=project_id,
+                limit=limit,
+                since=since,
+            )
+            # Deduplicate by ID
+            existing_ids = {e.id for e in episodes}
+            for ep in recent:
+                if ep.id not in existing_ids:
+                    episodes.append(ep)
+                    if len(episodes) >= limit:
+                        break
+
+        return [
+            MemoryContext.from_episodic_memory(ep, query=query)
+            for ep in episodes[:limit]
+        ]
+
+    async def _fetch_semantic(
+        self,
+        query: str,
+        project_id: UUID,
+        limit: int,
+        min_relevance: float,
+    ) -> list[MemoryContext]:
+        """Fetch from semantic memory."""
+        semantic = await self._get_semantic()
+
+        facts = await semantic.search_facts(
+            query=query,
+            project_id=project_id,
+            limit=limit,
+            min_confidence=min_relevance,
+        )
+
+        return [MemoryContext.from_semantic_memory(fact, query=query) for fact in facts]
+
+    async def _fetch_procedural(
+        self,
+        query: str,
+        project_id: UUID,
+        agent_type_id: UUID | None,
+        limit: int,
+    ) -> list[MemoryContext]:
+        """Fetch from procedural memory."""
+        procedural = await self._get_procedural()
+
+        procedures = await procedural.find_matching(
+            context=query,
+            project_id=project_id,
+            agent_type_id=agent_type_id,
+            limit=limit,
+        )
+
+        return [
+            MemoryContext.from_procedural_memory(proc, query=query)
+            for proc in procedures
+        ]
+
+    async def fetch_all_working(
+        self,
+        session_id: str,
+        project_id: UUID,
+        agent_instance_id: UUID | None = None,
+    ) -> list[MemoryContext]:
+        """
+        Fetch all working memory for a session.
+
+        Useful for including entire session state in context.
+
+        Args:
+            session_id: Session ID
+            project_id: Project scope
+            agent_instance_id: Optional agent instance scope
+
+        Returns:
+            List of MemoryContext for all working memory items
+        """
+        working = await WorkingMemory.for_session(
+            session_id=session_id,
+            project_id=str(project_id),
+            agent_instance_id=str(agent_instance_id) if agent_instance_id else None,
+        )
+
+        contexts: list[MemoryContext] = []
+        all_keys = await working.list_keys()
+
+        for key in all_keys:
+            value = await working.get(key)
+            if value is not None:
+                contexts.append(
+                    MemoryContext.from_working_memory(
+                        key=key,
+                        value=value,
+                        source=f"working:{session_id}",
+                    )
+                )
+
+        return contexts
+
+
+# Factory function
+async def get_memory_context_source(
+    session: AsyncSession,
+    embedding_generator: Any | None = None,
+) -> MemoryContextSource:
+    """Create a memory context source instance."""
+    return MemoryContextSource(
+        session=session,
+        embedding_generator=embedding_generator,
+    )

backend/app/services/memory/integration/lifecycle.py

@@ -0,0 +1,635 @@
+# app/services/memory/integration/lifecycle.py
+"""
+Agent Lifecycle Hooks for Memory System.
+
+Provides memory management hooks for agent lifecycle events:
+- spawn: Initialize working memory for new agent instance
+- pause: Checkpoint working memory state
+- resume: Restore working memory from checkpoint
+- terminate: Consolidate session to episodic memory
+"""
+
+import logging
+from collections.abc import Callable, Coroutine
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from typing import Any
+from uuid import UUID
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.services.memory.episodic import EpisodicMemory
+from app.services.memory.types import EpisodeCreate, Outcome
+from app.services.memory.working import WorkingMemory
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class LifecycleEvent:
+    """Event data for lifecycle hooks."""
+
+    event_type: str  # spawn, pause, resume, terminate
+    project_id: UUID
+    agent_instance_id: UUID
+    agent_type_id: UUID | None = None
+    session_id: str | None = None
+    timestamp: datetime = field(default_factory=lambda: datetime.now(UTC))
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+
+@dataclass
+class LifecycleResult:
+    """Result of a lifecycle operation."""
+
+    success: bool
+    event_type: str
+    message: str | None = None
+    data: dict[str, Any] = field(default_factory=dict)
+    duration_ms: float = 0.0
+
+
+# Type alias for lifecycle hooks
+LifecycleHook = Callable[[LifecycleEvent], Coroutine[Any, Any, None]]
+
+
+class LifecycleHooks:
+    """
+    Collection of lifecycle hooks.
+
+    Allows registration of custom hooks for lifecycle events.
+    Hooks are called after the core memory operations.
+    """
+
+    def __init__(self) -> None:
+        """Initialize lifecycle hooks."""
+        self._spawn_hooks: list[LifecycleHook] = []
+        self._pause_hooks: list[LifecycleHook] = []
+        self._resume_hooks: list[LifecycleHook] = []
+        self._terminate_hooks: list[LifecycleHook] = []
+
+    def on_spawn(self, hook: LifecycleHook) -> LifecycleHook:
+        """Register a spawn hook."""
+        self._spawn_hooks.append(hook)
+        return hook
+
+    def on_pause(self, hook: LifecycleHook) -> LifecycleHook:
+        """Register a pause hook."""
+        self._pause_hooks.append(hook)
+        return hook
+
+    def on_resume(self, hook: LifecycleHook) -> LifecycleHook:
+        """Register a resume hook."""
+        self._resume_hooks.append(hook)
+        return hook
+
+    def on_terminate(self, hook: LifecycleHook) -> LifecycleHook:
+        """Register a terminate hook."""
+        self._terminate_hooks.append(hook)
+        return hook
+
+    async def run_spawn_hooks(self, event: LifecycleEvent) -> None:
+        """Run all spawn hooks."""
+        for hook in self._spawn_hooks:
+            try:
+                await hook(event)
+            except Exception as e:
+                logger.warning(f"Spawn hook failed: {e}")
+
+    async def run_pause_hooks(self, event: LifecycleEvent) -> None:
+        """Run all pause hooks."""
+        for hook in self._pause_hooks:
+            try:
+                await hook(event)
+            except Exception as e:
+                logger.warning(f"Pause hook failed: {e}")
+
+    async def run_resume_hooks(self, event: LifecycleEvent) -> None:
+        """Run all resume hooks."""
+        for hook in self._resume_hooks:
+            try:
+                await hook(event)
+            except Exception as e:
+                logger.warning(f"Resume hook failed: {e}")
+
+    async def run_terminate_hooks(self, event: LifecycleEvent) -> None:
+        """Run all terminate hooks."""
+        for hook in self._terminate_hooks:
+            try:
+                await hook(event)
+            except Exception as e:
+                logger.warning(f"Terminate hook failed: {e}")
+
+
+class AgentLifecycleManager:
+    """
+    Manager for agent lifecycle and memory integration.
+
+    Handles memory operations during agent lifecycle events:
+    - spawn: Creates new working memory for the session
+    - pause: Saves working memory state to checkpoint
+    - resume: Restores working memory from checkpoint
+    - terminate: Consolidates working memory to episodic memory
+    """
+
+    # Key prefix for checkpoint storage
+    CHECKPOINT_PREFIX = "__checkpoint__"
+
+    def __init__(
+        self,
+        session: AsyncSession,
+        embedding_generator: Any | None = None,
+        hooks: LifecycleHooks | None = None,
+    ) -> None:
+        """
+        Initialize the lifecycle manager.
+
+        Args:
+            session: Database session
+            embedding_generator: Optional embedding generator
+            hooks: Optional lifecycle hooks
+        """
+        self._session = session
+        self._embedding_generator = embedding_generator
+        self._hooks = hooks or LifecycleHooks()
+
+        # Lazy-initialized services
+        self._episodic: EpisodicMemory | None = None
+
+    async def _get_episodic(self) -> EpisodicMemory:
+        """Get or create episodic memory service."""
+        if self._episodic is None:
+            self._episodic = await EpisodicMemory.create(
+                self._session,
+                self._embedding_generator,
+            )
+        return self._episodic
+
+    @property
+    def hooks(self) -> LifecycleHooks:
+        """Get the lifecycle hooks."""
+        return self._hooks
+
+    async def spawn(
+        self,
+        project_id: UUID,
+        agent_instance_id: UUID,
+        session_id: str,
+        agent_type_id: UUID | None = None,
+        initial_state: dict[str, Any] | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> LifecycleResult:
+        """
+        Handle agent spawn - initialize working memory.
+
+        Creates a new working memory instance for the agent session
+        and optionally populates it with initial state.
+
+        Args:
+            project_id: Project scope
+            agent_instance_id: Agent instance ID
+            session_id: Session ID for working memory
+            agent_type_id: Optional agent type ID
+            initial_state: Optional initial state to populate
+            metadata: Optional metadata for the event
+
+        Returns:
+            LifecycleResult with spawn outcome
+        """
+        start_time = datetime.now(UTC)
+
+        try:
+            # Create working memory for the session
+            working = await WorkingMemory.for_session(
+                session_id=session_id,
+                project_id=str(project_id),
+                agent_instance_id=str(agent_instance_id),
+            )
+
+            # Populate initial state if provided
+            items_set = 0
+            if initial_state:
+                for key, value in initial_state.items():
+                    await working.set(key, value)
+                    items_set += 1
+
+            # Create and run event hooks
+            event = LifecycleEvent(
+                event_type="spawn",
+                project_id=project_id,
+                agent_instance_id=agent_instance_id,
+                agent_type_id=agent_type_id,
+                session_id=session_id,
+                metadata=metadata or {},
+            )
+            await self._hooks.run_spawn_hooks(event)
+
+            duration_ms = (datetime.now(UTC) - start_time).total_seconds() * 1000
+
+            logger.info(
+                f"Agent {agent_instance_id} spawned with session {session_id}, "
+                f"initial state: {items_set} items"
+            )
+
+            return LifecycleResult(
+                success=True,
+                event_type="spawn",
+                message="Agent spawned successfully",
+                data={
+                    "session_id": session_id,
+                    "initial_items": items_set,
+                },
+                duration_ms=duration_ms,
+            )
+
+        except Exception as e:
+            logger.error(f"Spawn failed for agent {agent_instance_id}: {e}")
+            return LifecycleResult(
+                success=False,
+                event_type="spawn",
+                message=f"Spawn failed: {e}",
+            )
+
+    async def pause(
+        self,
+        project_id: UUID,
+        agent_instance_id: UUID,
+        session_id: str,
+        checkpoint_id: str | None = None,
+        metadata: dict[str, Any] | None = None,
+    ) -> LifecycleResult:
+        """
+        Handle agent pause - checkpoint working memory.
+
+        Saves the current working memory state to a checkpoint
+        that can be restored later with resume().
+
+        Args:
+            project_id: Project scope
+            agent_instance_id: Agent instance ID
+            session_id: Session ID
+            checkpoint_id: Optional checkpoint identifier
+            metadata: Optional metadata for the event
+
+        Returns:
+            LifecycleResult with checkpoint data
+        """
+        start_time = datetime.now(UTC)
+        checkpoint_id = checkpoint_id or f"checkpoint_{int(start_time.timestamp())}"
+
+        try:
+            working = await WorkingMemory.for_session(
+                session_id=session_id,
+                project_id=str(project_id),
+                agent_instance_id=str(agent_instance_id),
+            )
+
+            # Get all current state
+            all_keys = await working.list_keys()
+            # Filter out checkpoint keys
+            state_keys = [
+                k for k in all_keys if not k.startswith(self.CHECKPOINT_PREFIX)
+            ]
+
+            state: dict[str, Any] = {}
+            for key in state_keys:
+                value = await working.get(key)
+                if value is not None:
+                    state[key] = value
+
+            # Store checkpoint
+            checkpoint_key = f"{self.CHECKPOINT_PREFIX}{checkpoint_id}"
+            await working.set(
+                checkpoint_key,
+                {
+                    "state": state,
+                    "timestamp": start_time.isoformat(),
+                    "keys_count": len(state),
+                },
+                ttl_seconds=86400 * 7,  # Keep checkpoint for 7 days
+            )
+
+            # Run hooks
+            event = LifecycleEvent(
+                event_type="pause",
+                project_id=project_id,
+                agent_instance_id=agent_instance_id,
+                session_id=session_id,
+                metadata={**(metadata or {}), "checkpoint_id": checkpoint_id},
+            )
+            await self._hooks.run_pause_hooks(event)
+
+            duration_ms = (datetime.now(UTC) - start_time).total_seconds() * 1000
+
+            logger.info(
+                f"Agent {agent_instance_id} paused, checkpoint {checkpoint_id} "
+                f"saved with {len(state)} items"
+            )
+
+            return LifecycleResult(
+                success=True,
+                event_type="pause",
+                message="Agent paused successfully",
+                data={
+                    "checkpoint_id": checkpoint_id,
+                    "items_saved": len(state),
+                    "timestamp": start_time.isoformat(),
+                },
+                duration_ms=duration_ms,
+            )
+
+        except Exception as e:
+            logger.error(f"Pause failed for agent {agent_instance_id}: {e}")
+            return LifecycleResult(
+                success=False,
+                event_type="pause",
+                message=f"Pause failed: {e}",
+            )
+
+    async def resume(
+        self,
+        project_id: UUID,
+        agent_instance_id: UUID,
+        session_id: str,
+        checkpoint_id: str,
+        clear_current: bool = True,
+        metadata: dict[str, Any] | None = None,
+    ) -> LifecycleResult:
+        """
+        Handle agent resume - restore from checkpoint.
+
+        Restores working memory state from a previously saved checkpoint.
+
+        Args:
+            project_id: Project scope
+            agent_instance_id: Agent instance ID
+            session_id: Session ID
+            checkpoint_id: Checkpoint to restore from
+            clear_current: Whether to clear current state before restoring
+            metadata: Optional metadata for the event
+
+        Returns:
+            LifecycleResult with restore outcome
+        """
+        start_time = datetime.now(UTC)
+
+        try:
+            working = await WorkingMemory.for_session(
+                session_id=session_id,
+                project_id=str(project_id),
+                agent_instance_id=str(agent_instance_id),
+            )
+
+            # Get checkpoint
+            checkpoint_key = f"{self.CHECKPOINT_PREFIX}{checkpoint_id}"
+            checkpoint = await working.get(checkpoint_key)
+
+            if checkpoint is None:
+                return LifecycleResult(
+                    success=False,
+                    event_type="resume",
+                    message=f"Checkpoint '{checkpoint_id}' not found",
+                )
+
+            # Clear current state if requested
+            if clear_current:
+                all_keys = await working.list_keys()
+                for key in all_keys:
+                    if not key.startswith(self.CHECKPOINT_PREFIX):
+                        await working.delete(key)
+
+            # Restore state from checkpoint
+            state = checkpoint.get("state", {})
+            items_restored = 0
+            for key, value in state.items():
+                await working.set(key, value)
+                items_restored += 1
+
+            # Run hooks
+            event = LifecycleEvent(
+                event_type="resume",
+                project_id=project_id,
+                agent_instance_id=agent_instance_id,
+                session_id=session_id,
+                metadata={**(metadata or {}), "checkpoint_id": checkpoint_id},
+            )
+            await self._hooks.run_resume_hooks(event)
+
+            duration_ms = (datetime.now(UTC) - start_time).total_seconds() * 1000
+
+            logger.info(
+                f"Agent {agent_instance_id} resumed from checkpoint {checkpoint_id}, "
+                f"restored {items_restored} items"
+            )
+
+            return LifecycleResult(
+                success=True,
+                event_type="resume",
+                message="Agent resumed successfully",
+                data={
+                    "checkpoint_id": checkpoint_id,
+                    "items_restored": items_restored,
+                    "checkpoint_timestamp": checkpoint.get("timestamp"),
+                },
+                duration_ms=duration_ms,
+            )
+
+        except Exception as e:
+            logger.error(f"Resume failed for agent {agent_instance_id}: {e}")
+            return LifecycleResult(
+                success=False,
+                event_type="resume",
+                message=f"Resume failed: {e}",
+            )
+
+    async def terminate(
+        self,
+        project_id: UUID,
+        agent_instance_id: UUID,
+        session_id: str,
+        task_description: str | None = None,
+        outcome: Outcome = Outcome.SUCCESS,
+        lessons_learned: list[str] | None = None,
+        consolidate_to_episodic: bool = True,
+        cleanup_working: bool = True,
+        metadata: dict[str, Any] | None = None,
+    ) -> LifecycleResult:
+        """
+        Handle agent termination - consolidate to episodic memory.
+
+        Consolidates the session's working memory into an episodic memory
+        entry, then optionally cleans up the working memory.
+
+        Args:
+            project_id: Project scope
+            agent_instance_id: Agent instance ID
+            session_id: Session ID
+            task_description: Description of what was accomplished
+            outcome: Task outcome (SUCCESS, FAILURE, PARTIAL)
+            lessons_learned: Optional list of lessons learned
+            consolidate_to_episodic: Whether to create episodic entry
+            cleanup_working: Whether to clear working memory
+            metadata: Optional metadata for the event
+
+        Returns:
+            LifecycleResult with termination outcome
+        """
+        start_time = datetime.now(UTC)
+
+        try:
+            working = await WorkingMemory.for_session(
+                session_id=session_id,
+                project_id=str(project_id),
+                agent_instance_id=str(agent_instance_id),
+            )
+
+            # Gather session state for consolidation
+            all_keys = await working.list_keys()
+            state_keys = [
+                k for k in all_keys if not k.startswith(self.CHECKPOINT_PREFIX)
+            ]
+
+            session_state: dict[str, Any] = {}
+            for key in state_keys:
+                value = await working.get(key)
+                if value is not None:
+                    session_state[key] = value
+
+            episode_id: str | None = None
+
+            # Consolidate to episodic memory
+            if consolidate_to_episodic:
+                episodic = await self._get_episodic()
+
+                description = task_description or f"Session {session_id} completed"
+
+                episode_data = EpisodeCreate(
+                    project_id=project_id,
+                    agent_instance_id=agent_instance_id,
+                    session_id=session_id,
+                    task_type="session_completion",
+                    task_description=description[:500],
+                    outcome=outcome,
+                    outcome_details=f"Session terminated with {len(session_state)} state items",
+                    actions=[
+                        {
+                            "type": "session_terminate",
+                            "state_keys": list(session_state.keys()),
+                            "outcome": outcome.value,
+                        }
+                    ],
+                    context_summary=str(session_state)[:1000] if session_state else "",
+                    lessons_learned=lessons_learned or [],
+                    duration_seconds=0.0,  # Unknown at this point
+                    tokens_used=0,
+                    importance_score=0.6,  # Moderate importance for session ends
+                )
+
+                episode = await episodic.record_episode(episode_data)
+                episode_id = str(episode.id)
+
+            # Clean up working memory
+            items_cleared = 0
+            if cleanup_working:
+                for key in all_keys:
+                    await working.delete(key)
+                    items_cleared += 1
+
+            # Run hooks
+            event = LifecycleEvent(
+                event_type="terminate",
+                project_id=project_id,
+                agent_instance_id=agent_instance_id,
+                session_id=session_id,
+                metadata={**(metadata or {}), "episode_id": episode_id},
+            )
+            await self._hooks.run_terminate_hooks(event)
+
+            duration_ms = (datetime.now(UTC) - start_time).total_seconds() * 1000
+
+            logger.info(
+                f"Agent {agent_instance_id} terminated, session {session_id} "
+                f"consolidated to episode {episode_id}"
+            )
+
+            return LifecycleResult(
+                success=True,
+                event_type="terminate",
+                message="Agent terminated successfully",
+                data={
+                    "episode_id": episode_id,
+                    "state_items_consolidated": len(session_state),
+                    "items_cleared": items_cleared,
+                    "outcome": outcome.value,
+                },
+                duration_ms=duration_ms,
+            )
+
+        except Exception as e:
+            logger.error(f"Terminate failed for agent {agent_instance_id}: {e}")
+            return LifecycleResult(
+                success=False,
+                event_type="terminate",
+                message=f"Terminate failed: {e}",
+            )
+
+    async def list_checkpoints(
+        self,
+        project_id: UUID,
+        agent_instance_id: UUID,
+        session_id: str,
+    ) -> list[dict[str, Any]]:
+        """
+        List available checkpoints for a session.
+
+        Args:
+            project_id: Project scope
+            agent_instance_id: Agent instance ID
+            session_id: Session ID
+
+        Returns:
+            List of checkpoint metadata dicts
+        """
+        working = await WorkingMemory.for_session(
+            session_id=session_id,
+            project_id=str(project_id),
+            agent_instance_id=str(agent_instance_id),
+        )
+
+        all_keys = await working.list_keys()
+        checkpoints: list[dict[str, Any]] = []
+
+        for key in all_keys:
+            if key.startswith(self.CHECKPOINT_PREFIX):
+                checkpoint_id = key[len(self.CHECKPOINT_PREFIX) :]
+                checkpoint = await working.get(key)
+                if checkpoint:
+                    checkpoints.append(
+                        {
+                            "checkpoint_id": checkpoint_id,
+                            "timestamp": checkpoint.get("timestamp"),
+                            "keys_count": checkpoint.get("keys_count", 0),
+                        }
+                    )
+
+        # Sort by timestamp (newest first)
+        checkpoints.sort(
+            key=lambda c: c.get("timestamp", ""),
+            reverse=True,
+        )
+
+        return checkpoints
+
+
+# Factory function
+async def get_lifecycle_manager(
+    session: AsyncSession,
+    embedding_generator: Any | None = None,
+    hooks: LifecycleHooks | None = None,
+) -> AgentLifecycleManager:
+    """Create a lifecycle manager instance."""
+    return AgentLifecycleManager(
+        session=session,
+        embedding_generator=embedding_generator,
+        hooks=hooks,
+    )

backend/app/services/memory/manager.py

@@ -0,0 +1,606 @@
+"""
+Memory Manager
+
+Facade for the Agent Memory System providing unified access
+to all memory types and operations.
+"""
+
+import logging
+from typing import Any
+from uuid import UUID
+
+from .config import MemorySettings, get_memory_settings
+from .types import (
+    Episode,
+    EpisodeCreate,
+    Fact,
+    FactCreate,
+    MemoryStats,
+    MemoryType,
+    Outcome,
+    Procedure,
+    ProcedureCreate,
+    RetrievalResult,
+    ScopeContext,
+    ScopeLevel,
+    TaskState,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class MemoryManager:
+    """
+    Unified facade for the Agent Memory System.
+
+    Provides a single entry point for all memory operations across
+    working, episodic, semantic, and procedural memory types.
+
+    Usage:
+        manager = MemoryManager.create()
+
+        # Working memory
+        await manager.set_working("key", {"data": "value"})
+        value = await manager.get_working("key")
+
+        # Episodic memory
+        episode = await manager.record_episode(episode_data)
+        similar = await manager.search_episodes("query")
+
+        # Semantic memory
+        fact = await manager.store_fact(fact_data)
+        facts = await manager.search_facts("query")
+
+        # Procedural memory
+        procedure = await manager.record_procedure(procedure_data)
+        procedures = await manager.find_procedures("context")
+    """
+
+    def __init__(
+        self,
+        settings: MemorySettings,
+        scope: ScopeContext,
+    ) -> None:
+        """
+        Initialize the MemoryManager.
+
+        Args:
+            settings: Memory configuration settings
+            scope: The scope context for this manager instance
+        """
+        self._settings = settings
+        self._scope = scope
+        self._initialized = False
+
+        # These will be initialized when the respective sub-modules are implemented
+        self._working_memory: Any | None = None
+        self._episodic_memory: Any | None = None
+        self._semantic_memory: Any | None = None
+        self._procedural_memory: Any | None = None
+
+        logger.debug(
+            "MemoryManager created for scope %s:%s",
+            scope.scope_type.value,
+            scope.scope_id,
+        )
+
+    @classmethod
+    def create(
+        cls,
+        scope_type: ScopeLevel = ScopeLevel.SESSION,
+        scope_id: str = "default",
+        parent_scope: ScopeContext | None = None,
+        settings: MemorySettings | None = None,
+    ) -> "MemoryManager":
+        """
+        Create a new MemoryManager instance.
+
+        Args:
+            scope_type: The scope level for this manager
+            scope_id: The scope identifier
+            parent_scope: Optional parent scope for inheritance
+            settings: Optional custom settings (uses global if not provided)
+
+        Returns:
+            A new MemoryManager instance
+        """
+        if settings is None:
+            settings = get_memory_settings()
+
+        scope = ScopeContext(
+            scope_type=scope_type,
+            scope_id=scope_id,
+            parent=parent_scope,
+        )
+
+        return cls(settings=settings, scope=scope)
+
+    @classmethod
+    def for_session(
+        cls,
+        session_id: str,
+        agent_instance_id: UUID | None = None,
+        project_id: UUID | None = None,
+    ) -> "MemoryManager":
+        """
+        Create a MemoryManager for a specific session.
+
+        Builds the appropriate scope hierarchy based on provided IDs.
+
+        Args:
+            session_id: The session identifier
+            agent_instance_id: Optional agent instance ID
+            project_id: Optional project ID
+
+        Returns:
+            A MemoryManager configured for the session scope
+        """
+        settings = get_memory_settings()
+
+        # Build scope hierarchy
+        parent: ScopeContext | None = None
+
+        if project_id:
+            parent = ScopeContext(
+                scope_type=ScopeLevel.PROJECT,
+                scope_id=str(project_id),
+                parent=ScopeContext(
+                    scope_type=ScopeLevel.GLOBAL,
+                    scope_id="global",
+                ),
+            )
+
+        if agent_instance_id:
+            parent = ScopeContext(
+                scope_type=ScopeLevel.AGENT_INSTANCE,
+                scope_id=str(agent_instance_id),
+                parent=parent,
+            )
+
+        scope = ScopeContext(
+            scope_type=ScopeLevel.SESSION,
+            scope_id=session_id,
+            parent=parent,
+        )
+
+        return cls(settings=settings, scope=scope)
+
+    @property
+    def scope(self) -> ScopeContext:
+        """Get the current scope context."""
+        return self._scope
+
+    @property
+    def settings(self) -> MemorySettings:
+        """Get the memory settings."""
+        return self._settings
+
+    # =========================================================================
+    # Working Memory Operations
+    # =========================================================================
+
+    async def set_working(
+        self,
+        key: str,
+        value: Any,
+        ttl_seconds: int | None = None,
+    ) -> None:
+        """
+        Set a value in working memory.
+
+        Args:
+            key: The key to store the value under
+            value: The value to store (must be JSON serializable)
+            ttl_seconds: Optional TTL (uses default if not provided)
+        """
+        # Placeholder - will be implemented in #89
+        logger.debug("set_working called for key=%s (not yet implemented)", key)
+        raise NotImplementedError("Working memory not yet implemented")
+
+    async def get_working(
+        self,
+        key: str,
+        default: Any = None,
+    ) -> Any:
+        """
+        Get a value from working memory.
+
+        Args:
+            key: The key to retrieve
+            default: Default value if key not found
+
+        Returns:
+            The stored value or default
+        """
+        # Placeholder - will be implemented in #89
+        logger.debug("get_working called for key=%s (not yet implemented)", key)
+        raise NotImplementedError("Working memory not yet implemented")
+
+    async def delete_working(self, key: str) -> bool:
+        """
+        Delete a value from working memory.
+
+        Args:
+            key: The key to delete
+
+        Returns:
+            True if the key was deleted, False if not found
+        """
+        # Placeholder - will be implemented in #89
+        logger.debug("delete_working called for key=%s (not yet implemented)", key)
+        raise NotImplementedError("Working memory not yet implemented")
+
+    async def set_task_state(self, state: TaskState) -> None:
+        """
+        Set the current task state in working memory.
+
+        Args:
+            state: The task state to store
+        """
+        # Placeholder - will be implemented in #89
+        logger.debug(
+            "set_task_state called for task=%s (not yet implemented)",
+            state.task_id,
+        )
+        raise NotImplementedError("Working memory not yet implemented")
+
+    async def get_task_state(self) -> TaskState | None:
+        """
+        Get the current task state from working memory.
+
+        Returns:
+            The current task state or None
+        """
+        # Placeholder - will be implemented in #89
+        logger.debug("get_task_state called (not yet implemented)")
+        raise NotImplementedError("Working memory not yet implemented")
+
+    async def create_checkpoint(self) -> str:
+        """
+        Create a checkpoint of the current working memory state.
+
+        Returns:
+            The checkpoint ID
+        """
+        # Placeholder - will be implemented in #89
+        logger.debug("create_checkpoint called (not yet implemented)")
+        raise NotImplementedError("Working memory not yet implemented")
+
+    async def restore_checkpoint(self, checkpoint_id: str) -> None:
+        """
+        Restore working memory from a checkpoint.
+
+        Args:
+            checkpoint_id: The checkpoint to restore from
+        """
+        # Placeholder - will be implemented in #89
+        logger.debug(
+            "restore_checkpoint called for id=%s (not yet implemented)",
+            checkpoint_id,
+        )
+        raise NotImplementedError("Working memory not yet implemented")
+
+    # =========================================================================
+    # Episodic Memory Operations
+    # =========================================================================
+
+    async def record_episode(self, episode: EpisodeCreate) -> Episode:
+        """
+        Record a new episode in episodic memory.
+
+        Args:
+            episode: The episode data to record
+
+        Returns:
+            The created episode with ID
+        """
+        # Placeholder - will be implemented in #90
+        logger.debug(
+            "record_episode called for task=%s (not yet implemented)",
+            episode.task_type,
+        )
+        raise NotImplementedError("Episodic memory not yet implemented")
+
+    async def search_episodes(
+        self,
+        query: str,
+        limit: int | None = None,
+    ) -> RetrievalResult[Episode]:
+        """
+        Search for similar episodes.
+
+        Args:
+            query: The search query
+            limit: Maximum results to return
+
+        Returns:
+            Retrieval result with matching episodes
+        """
+        # Placeholder - will be implemented in #90
+        logger.debug(
+            "search_episodes called for query=%s (not yet implemented)",
+            query[:50],
+        )
+        raise NotImplementedError("Episodic memory not yet implemented")
+
+    async def get_recent_episodes(
+        self,
+        limit: int = 10,
+    ) -> list[Episode]:
+        """
+        Get the most recent episodes.
+
+        Args:
+            limit: Maximum episodes to return
+
+        Returns:
+            List of recent episodes
+        """
+        # Placeholder - will be implemented in #90
+        logger.debug("get_recent_episodes called (not yet implemented)")
+        raise NotImplementedError("Episodic memory not yet implemented")
+
+    async def get_episodes_by_outcome(
+        self,
+        outcome: Outcome,
+        limit: int = 10,
+    ) -> list[Episode]:
+        """
+        Get episodes by outcome.
+
+        Args:
+            outcome: The outcome to filter by
+            limit: Maximum episodes to return
+
+        Returns:
+            List of episodes with the specified outcome
+        """
+        # Placeholder - will be implemented in #90
+        logger.debug(
+            "get_episodes_by_outcome called for outcome=%s (not yet implemented)",
+            outcome.value,
+        )
+        raise NotImplementedError("Episodic memory not yet implemented")
+
+    # =========================================================================
+    # Semantic Memory Operations
+    # =========================================================================
+
+    async def store_fact(self, fact: FactCreate) -> Fact:
+        """
+        Store a new fact in semantic memory.
+
+        Args:
+            fact: The fact data to store
+
+        Returns:
+            The created fact with ID
+        """
+        # Placeholder - will be implemented in #91
+        logger.debug(
+            "store_fact called for %s %s %s (not yet implemented)",
+            fact.subject,
+            fact.predicate,
+            fact.object,
+        )
+        raise NotImplementedError("Semantic memory not yet implemented")
+
+    async def search_facts(
+        self,
+        query: str,
+        limit: int | None = None,
+    ) -> RetrievalResult[Fact]:
+        """
+        Search for facts matching a query.
+
+        Args:
+            query: The search query
+            limit: Maximum results to return
+
+        Returns:
+            Retrieval result with matching facts
+        """
+        # Placeholder - will be implemented in #91
+        logger.debug(
+            "search_facts called for query=%s (not yet implemented)",
+            query[:50],
+        )
+        raise NotImplementedError("Semantic memory not yet implemented")
+
+    async def get_facts_by_entity(
+        self,
+        entity: str,
+        limit: int = 20,
+    ) -> list[Fact]:
+        """
+        Get facts related to an entity.
+
+        Args:
+            entity: The entity to search for
+            limit: Maximum facts to return
+
+        Returns:
+            List of facts mentioning the entity
+        """
+        # Placeholder - will be implemented in #91
+        logger.debug(
+            "get_facts_by_entity called for entity=%s (not yet implemented)",
+            entity,
+        )
+        raise NotImplementedError("Semantic memory not yet implemented")
+
+    async def reinforce_fact(self, fact_id: UUID) -> Fact:
+        """
+        Reinforce a fact (increase confidence from repeated learning).
+
+        Args:
+            fact_id: The fact to reinforce
+
+        Returns:
+            The updated fact
+        """
+        # Placeholder - will be implemented in #91
+        logger.debug(
+            "reinforce_fact called for id=%s (not yet implemented)",
+            fact_id,
+        )
+        raise NotImplementedError("Semantic memory not yet implemented")
+
+    # =========================================================================
+    # Procedural Memory Operations
+    # =========================================================================
+
+    async def record_procedure(self, procedure: ProcedureCreate) -> Procedure:
+        """
+        Record a new procedure.
+
+        Args:
+            procedure: The procedure data to record
+
+        Returns:
+            The created procedure with ID
+        """
+        # Placeholder - will be implemented in #92
+        logger.debug(
+            "record_procedure called for name=%s (not yet implemented)",
+            procedure.name,
+        )
+        raise NotImplementedError("Procedural memory not yet implemented")
+
+    async def find_procedures(
+        self,
+        context: str,
+        limit: int = 5,
+    ) -> list[Procedure]:
+        """
+        Find procedures matching the current context.
+
+        Args:
+            context: The context to match against
+            limit: Maximum procedures to return
+
+        Returns:
+            List of matching procedures sorted by success rate
+        """
+        # Placeholder - will be implemented in #92
+        logger.debug(
+            "find_procedures called for context=%s (not yet implemented)",
+            context[:50],
+        )
+        raise NotImplementedError("Procedural memory not yet implemented")
+
+    async def record_procedure_outcome(
+        self,
+        procedure_id: UUID,
+        success: bool,
+    ) -> None:
+        """
+        Record the outcome of using a procedure.
+
+        Args:
+            procedure_id: The procedure that was used
+            success: Whether the procedure succeeded
+        """
+        # Placeholder - will be implemented in #92
+        logger.debug(
+            "record_procedure_outcome called for id=%s success=%s (not yet implemented)",
+            procedure_id,
+            success,
+        )
+        raise NotImplementedError("Procedural memory not yet implemented")
+
+    # =========================================================================
+    # Cross-Memory Operations
+    # =========================================================================
+
+    async def recall(
+        self,
+        query: str,
+        memory_types: list[MemoryType] | None = None,
+        limit: int = 10,
+    ) -> dict[MemoryType, list[Any]]:
+        """
+        Recall memories across multiple memory types.
+
+        Args:
+            query: The search query
+            memory_types: Memory types to search (all if not specified)
+            limit: Maximum results per type
+
+        Returns:
+            Dictionary mapping memory types to results
+        """
+        # Placeholder - will be implemented in #97 (Component Integration)
+        logger.debug("recall called for query=%s (not yet implemented)", query[:50])
+        raise NotImplementedError("Cross-memory recall not yet implemented")
+
+    async def get_stats(
+        self,
+        memory_type: MemoryType | None = None,
+    ) -> list[MemoryStats]:
+        """
+        Get memory statistics.
+
+        Args:
+            memory_type: Specific type or all if not specified
+
+        Returns:
+            List of statistics for requested memory types
+        """
+        # Placeholder - will be implemented in #100 (Metrics & Observability)
+        logger.debug("get_stats called (not yet implemented)")
+        raise NotImplementedError("Memory stats not yet implemented")
+
+    # =========================================================================
+    # Lifecycle Operations
+    # =========================================================================
+
+    async def initialize(self) -> None:
+        """
+        Initialize the memory manager and its backends.
+
+        Should be called before using the manager.
+        """
+        if self._initialized:
+            logger.debug("MemoryManager already initialized")
+            return
+
+        logger.info(
+            "Initializing MemoryManager for scope %s:%s",
+            self._scope.scope_type.value,
+            self._scope.scope_id,
+        )
+
+        # TODO: Initialize backends when implemented
+
+        self._initialized = True
+        logger.info("MemoryManager initialized successfully")
+
+    async def close(self) -> None:
+        """
+        Close the memory manager and release resources.
+
+        Should be called when done using the manager.
+        """
+        if not self._initialized:
+            return
+
+        logger.info(
+            "Closing MemoryManager for scope %s:%s",
+            self._scope.scope_type.value,
+            self._scope.scope_id,
+        )
+
+        # TODO: Close backends when implemented
+
+        self._initialized = False
+        logger.info("MemoryManager closed successfully")
+
+    async def __aenter__(self) -> "MemoryManager":
+        """Async context manager entry."""
+        await self.initialize()
+        return self
+
+    async def __aexit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
+        """Async context manager exit."""
+        await self.close()

backend/app/services/memory/mcp/__init__.py

@@ -0,0 +1,40 @@
+# app/services/memory/mcp/__init__.py
+"""
+MCP Tools for Agent Memory System.
+
+Exposes memory operations as MCP-compatible tools that agents can invoke:
+- remember: Store data in memory
+- recall: Retrieve from memory
+- forget: Remove from memory
+- reflect: Analyze patterns
+- get_memory_stats: Usage statistics
+- search_procedures: Find relevant procedures
+- record_outcome: Record task success/failure
+"""
+
+from .service import MemoryToolService, get_memory_tool_service
+from .tools import (
+    MEMORY_TOOL_DEFINITIONS,
+    ForgetArgs,
+    GetMemoryStatsArgs,
+    MemoryToolDefinition,
+    RecallArgs,
+    RecordOutcomeArgs,
+    ReflectArgs,
+    RememberArgs,
+    SearchProceduresArgs,
+)
+
+__all__ = [
+    "MEMORY_TOOL_DEFINITIONS",
+    "ForgetArgs",
+    "GetMemoryStatsArgs",
+    "MemoryToolDefinition",
+    "MemoryToolService",
+    "RecallArgs",
+    "RecordOutcomeArgs",
+    "ReflectArgs",
+    "RememberArgs",
+    "SearchProceduresArgs",
+    "get_memory_tool_service",
+]

backend/app/services/memory/mcp/tools.py

@@ -0,0 +1,485 @@
+# app/services/memory/mcp/tools.py
+"""
+MCP Tool Definitions for Agent Memory System.
+
+Defines the schema and metadata for memory-related MCP tools.
+These tools are invoked by AI agents to interact with the memory system.
+"""
+
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+# OutcomeType alias - uses core Outcome enum from types module for consistency
+from app.services.memory.types import Outcome as OutcomeType
+
+
+class MemoryType(str, Enum):
+    """Types of memory for storage operations."""
+
+    WORKING = "working"
+    EPISODIC = "episodic"
+    SEMANTIC = "semantic"
+    PROCEDURAL = "procedural"
+
+
+class AnalysisType(str, Enum):
+    """Types of pattern analysis for the reflect tool."""
+
+    RECENT_PATTERNS = "recent_patterns"
+    SUCCESS_FACTORS = "success_factors"
+    FAILURE_PATTERNS = "failure_patterns"
+    COMMON_PROCEDURES = "common_procedures"
+    LEARNING_PROGRESS = "learning_progress"
+
+
+# ============================================================================
+# Tool Argument Schemas (Pydantic models for validation)
+# ============================================================================
+
+
+class RememberArgs(BaseModel):
+    """Arguments for the 'remember' tool."""
+
+    memory_type: MemoryType = Field(
+        ...,
+        description="Type of memory to store in: working, episodic, semantic, or procedural",
+    )
+    content: str = Field(
+        ...,
+        description="The content to remember. Can be text, facts, or procedure steps.",
+        min_length=1,
+        max_length=10000,
+    )
+    key: str | None = Field(
+        None,
+        description="Optional key for working memory entries. Required for working memory type.",
+        max_length=256,
+    )
+    importance: float = Field(
+        0.5,
+        description="Importance score from 0.0 (low) to 1.0 (critical)",
+        ge=0.0,
+        le=1.0,
+    )
+    ttl_seconds: int | None = Field(
+        None,
+        description="Time-to-live in seconds for working memory. None for permanent storage.",
+        ge=1,
+        le=86400 * 30,  # Max 30 days
+    )
+    metadata: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional metadata to store with the memory",
+    )
+    # For semantic memory (facts)
+    subject: str | None = Field(
+        None,
+        description="Subject of the fact (for semantic memory)",
+        max_length=256,
+    )
+    predicate: str | None = Field(
+        None,
+        description="Predicate/relationship (for semantic memory)",
+        max_length=256,
+    )
+    object_value: str | None = Field(
+        None,
+        description="Object of the fact (for semantic memory)",
+        max_length=1000,
+    )
+    # For procedural memory
+    trigger: str | None = Field(
+        None,
+        description="Trigger condition for the procedure (for procedural memory)",
+        max_length=500,
+    )
+    steps: list[dict[str, Any]] | None = Field(
+        None,
+        description="Procedure steps as a list of action dictionaries",
+    )
+
+
+class RecallArgs(BaseModel):
+    """Arguments for the 'recall' tool."""
+
+    query: str = Field(
+        ...,
+        description="Search query to find relevant memories",
+        min_length=1,
+        max_length=1000,
+    )
+    memory_types: list[MemoryType] = Field(
+        default_factory=lambda: [MemoryType.EPISODIC, MemoryType.SEMANTIC],
+        description="Types of memory to search in",
+    )
+    limit: int = Field(
+        10,
+        description="Maximum number of results to return",
+        ge=1,
+        le=100,
+    )
+    min_relevance: float = Field(
+        0.0,
+        description="Minimum relevance score (0.0-1.0) for results",
+        ge=0.0,
+        le=1.0,
+    )
+    filters: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Additional filters (e.g., outcome, task_type, date range)",
+    )
+    include_context: bool = Field(
+        True,
+        description="Whether to include surrounding context in results",
+    )
+
+
+class ForgetArgs(BaseModel):
+    """Arguments for the 'forget' tool."""
+
+    memory_type: MemoryType = Field(
+        ...,
+        description="Type of memory to remove from",
+    )
+    key: str | None = Field(
+        None,
+        description="Key to remove (for working memory)",
+        max_length=256,
+    )
+    memory_id: str | None = Field(
+        None,
+        description="Specific memory ID to remove (for episodic/semantic/procedural)",
+    )
+    pattern: str | None = Field(
+        None,
+        description="Pattern to match for bulk removal (use with caution)",
+        max_length=500,
+    )
+    confirm_bulk: bool = Field(
+        False,
+        description="Must be True to confirm bulk deletion when using pattern",
+    )
+
+
+class ReflectArgs(BaseModel):
+    """Arguments for the 'reflect' tool."""
+
+    analysis_type: AnalysisType = Field(
+        ...,
+        description="Type of pattern analysis to perform",
+    )
+    scope: str | None = Field(
+        None,
+        description="Optional scope to limit analysis (e.g., task_type, time range)",
+        max_length=500,
+    )
+    depth: int = Field(
+        3,
+        description="Depth of analysis (1=surface, 5=deep)",
+        ge=1,
+        le=5,
+    )
+    include_examples: bool = Field(
+        True,
+        description="Whether to include example memories in the analysis",
+    )
+    max_items: int = Field(
+        10,
+        description="Maximum number of patterns/examples to analyze",
+        ge=1,
+        le=50,
+    )
+
+
+class GetMemoryStatsArgs(BaseModel):
+    """Arguments for the 'get_memory_stats' tool."""
+
+    include_breakdown: bool = Field(
+        True,
+        description="Include breakdown by memory type",
+    )
+    include_recent_activity: bool = Field(
+        True,
+        description="Include recent memory activity summary",
+    )
+    time_range_days: int = Field(
+        7,
+        description="Time range for activity analysis in days",
+        ge=1,
+        le=90,
+    )
+
+
+class SearchProceduresArgs(BaseModel):
+    """Arguments for the 'search_procedures' tool."""
+
+    trigger: str = Field(
+        ...,
+        description="Trigger or situation to find procedures for",
+        min_length=1,
+        max_length=500,
+    )
+    task_type: str | None = Field(
+        None,
+        description="Optional task type to filter procedures",
+        max_length=100,
+    )
+    min_success_rate: float = Field(
+        0.5,
+        description="Minimum success rate (0.0-1.0) for returned procedures",
+        ge=0.0,
+        le=1.0,
+    )
+    limit: int = Field(
+        5,
+        description="Maximum number of procedures to return",
+        ge=1,
+        le=20,
+    )
+    include_steps: bool = Field(
+        True,
+        description="Whether to include detailed steps in the response",
+    )
+
+
+class RecordOutcomeArgs(BaseModel):
+    """Arguments for the 'record_outcome' tool."""
+
+    task_type: str = Field(
+        ...,
+        description="Type of task that was executed",
+        min_length=1,
+        max_length=100,
+    )
+    outcome: OutcomeType = Field(
+        ...,
+        description="Outcome of the task execution",
+    )
+    procedure_id: str | None = Field(
+        None,
+        description="ID of the procedure that was followed (if any)",
+    )
+    context: dict[str, Any] = Field(
+        default_factory=dict,
+        description="Context in which the task was executed",
+    )
+    lessons_learned: str | None = Field(
+        None,
+        description="What was learned from this execution",
+        max_length=2000,
+    )
+    duration_seconds: float | None = Field(
+        None,
+        description="How long the task took to execute",
+        ge=0.0,
+    )
+    error_details: str | None = Field(
+        None,
+        description="Details about any errors encountered (for failures)",
+        max_length=2000,
+    )
+
+
+# ============================================================================
+# Tool Definition Structure
+# ============================================================================
+
+
+@dataclass
+class MemoryToolDefinition:
+    """Definition of an MCP tool for the memory system."""
+
+    name: str
+    description: str
+    args_schema: type[BaseModel]
+    input_schema: dict[str, Any] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        """Generate input schema from Pydantic model."""
+        if not self.input_schema:
+            self.input_schema = self.args_schema.model_json_schema()
+
+    def to_mcp_format(self) -> dict[str, Any]:
+        """Convert to MCP tool format."""
+        return {
+            "name": self.name,
+            "description": self.description,
+            "inputSchema": self.input_schema,
+        }
+
+    def validate_args(self, args: dict[str, Any]) -> BaseModel:
+        """Validate and parse arguments."""
+        return self.args_schema.model_validate(args)
+
+
+# ============================================================================
+# Tool Definitions
+# ============================================================================
+
+
+REMEMBER_TOOL = MemoryToolDefinition(
+    name="remember",
+    description="""Store information in the agent's memory system.
+
+Use this tool to:
+- Store temporary data in working memory (key-value with optional TTL)
+- Record important events in episodic memory (automatically done on session end)
+- Store facts/knowledge in semantic memory (subject-predicate-object triples)
+- Save procedures in procedural memory (trigger conditions and steps)
+
+Examples:
+- Working memory: {"memory_type": "working", "key": "current_task", "content": "Implementing auth", "ttl_seconds": 3600}
+- Semantic fact: {"memory_type": "semantic", "subject": "User", "predicate": "prefers", "object_value": "dark mode", "content": "User preference noted"}
+- Procedure: {"memory_type": "procedural", "trigger": "When creating a new file", "steps": [{"action": "check_exists"}, {"action": "create"}], "content": "File creation procedure"}
+""",
+    args_schema=RememberArgs,
+)
+
+
+RECALL_TOOL = MemoryToolDefinition(
+    name="recall",
+    description="""Retrieve information from the agent's memory system.
+
+Use this tool to:
+- Search for relevant past experiences (episodic)
+- Look up known facts and knowledge (semantic)
+- Find applicable procedures for current task (procedural)
+- Get current session state (working)
+
+The query supports semantic search - describe what you're looking for in natural language.
+
+Examples:
+- {"query": "How did I handle authentication errors before?", "memory_types": ["episodic"]}
+- {"query": "What are the user's preferences?", "memory_types": ["semantic"], "limit": 5}
+- {"query": "database connection", "memory_types": ["episodic", "semantic", "procedural"], "filters": {"outcome": "success"}}
+""",
+    args_schema=RecallArgs,
+)
+
+
+FORGET_TOOL = MemoryToolDefinition(
+    name="forget",
+    description="""Remove information from the agent's memory system.
+
+Use this tool to:
+- Clear temporary working memory entries
+- Remove specific memories by ID
+- Bulk remove memories matching a pattern (requires confirmation)
+
+WARNING: Deletion is permanent. Use with caution.
+
+Examples:
+- Working memory: {"memory_type": "working", "key": "temp_calculation"}
+- Specific memory: {"memory_type": "episodic", "memory_id": "ep-123"}
+- Bulk (requires confirm): {"memory_type": "working", "pattern": "cache_*", "confirm_bulk": true}
+""",
+    args_schema=ForgetArgs,
+)
+
+
+REFLECT_TOOL = MemoryToolDefinition(
+    name="reflect",
+    description="""Analyze patterns in the agent's memory to gain insights.
+
+Use this tool to:
+- Identify patterns in recent work
+- Understand what leads to success/failure
+- Learn from past experiences
+- Track learning progress over time
+
+Analysis types:
+- recent_patterns: What patterns appear in recent work
+- success_factors: What conditions lead to success
+- failure_patterns: What causes failures and how to avoid them
+- common_procedures: Most frequently used procedures
+- learning_progress: How knowledge has grown over time
+
+Examples:
+- {"analysis_type": "success_factors", "scope": "code_review", "depth": 3}
+- {"analysis_type": "failure_patterns", "include_examples": true, "max_items": 5}
+""",
+    args_schema=ReflectArgs,
+)
+
+
+GET_MEMORY_STATS_TOOL = MemoryToolDefinition(
+    name="get_memory_stats",
+    description="""Get statistics about the agent's memory usage.
+
+Returns information about:
+- Total memories stored by type
+- Storage utilization
+- Recent activity summary
+- Memory health indicators
+
+Use this to understand memory capacity and usage patterns.
+
+Examples:
+- {"include_breakdown": true, "include_recent_activity": true}
+- {"time_range_days": 30, "include_breakdown": true}
+""",
+    args_schema=GetMemoryStatsArgs,
+)
+
+
+SEARCH_PROCEDURES_TOOL = MemoryToolDefinition(
+    name="search_procedures",
+    description="""Find relevant procedures for a given situation.
+
+Use this tool when you need to:
+- Find the best way to handle a situation
+- Look up proven approaches to problems
+- Get step-by-step guidance for tasks
+
+Returns procedures ranked by relevance and success rate.
+
+Examples:
+- {"trigger": "Deploying to production", "min_success_rate": 0.8}
+- {"trigger": "Handling merge conflicts", "task_type": "git_operations", "limit": 3}
+""",
+    args_schema=SearchProceduresArgs,
+)
+
+
+RECORD_OUTCOME_TOOL = MemoryToolDefinition(
+    name="record_outcome",
+    description="""Record the outcome of a task execution.
+
+Use this tool after completing a task to:
+- Update procedure success/failure rates
+- Store lessons learned for future reference
+- Improve procedure recommendations
+
+This helps the memory system learn from experience.
+
+Examples:
+- {"task_type": "code_review", "outcome": "success", "lessons_learned": "Breaking changes caught early"}
+- {"task_type": "deployment", "outcome": "failure", "error_details": "Database migration timeout", "lessons_learned": "Need to test migrations locally first"}
+""",
+    args_schema=RecordOutcomeArgs,
+)
+
+
+# All tool definitions in a dictionary for easy lookup
+MEMORY_TOOL_DEFINITIONS: dict[str, MemoryToolDefinition] = {
+    "remember": REMEMBER_TOOL,
+    "recall": RECALL_TOOL,
+    "forget": FORGET_TOOL,
+    "reflect": REFLECT_TOOL,
+    "get_memory_stats": GET_MEMORY_STATS_TOOL,
+    "search_procedures": SEARCH_PROCEDURES_TOOL,
+    "record_outcome": RECORD_OUTCOME_TOOL,
+}
+
+
+def get_all_tool_schemas() -> list[dict[str, Any]]:
+    """Get MCP-formatted schemas for all memory tools."""
+    return [tool.to_mcp_format() for tool in MEMORY_TOOL_DEFINITIONS.values()]
+
+
+def get_tool_definition(name: str) -> MemoryToolDefinition | None:
+    """Get a specific tool definition by name."""
+    return MEMORY_TOOL_DEFINITIONS.get(name)

backend/app/services/memory/metrics/__init__.py

@@ -0,0 +1,18 @@
+# app/services/memory/metrics/__init__.py
+"""Memory Metrics module."""
+
+from .collector import (
+    MemoryMetrics,
+    get_memory_metrics,
+    record_memory_operation,
+    record_retrieval,
+    reset_memory_metrics,
+)
+
+__all__ = [
+    "MemoryMetrics",
+    "get_memory_metrics",
+    "record_memory_operation",
+    "record_retrieval",
+    "reset_memory_metrics",
+]

backend/app/services/memory/metrics/collector.py

@@ -0,0 +1,542 @@
+# app/services/memory/metrics/collector.py
+"""
+Memory Metrics Collector
+
+Collects and exposes metrics for the memory system.
+"""
+
+import asyncio
+import logging
+from collections import Counter, defaultdict, deque
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from enum import Enum
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+class MetricType(str, Enum):
+    """Types of metrics."""
+
+    COUNTER = "counter"
+    GAUGE = "gauge"
+    HISTOGRAM = "histogram"
+
+
+@dataclass
+class MetricValue:
+    """A single metric value."""
+
+    name: str
+    metric_type: MetricType
+    value: float
+    labels: dict[str, str] = field(default_factory=dict)
+    timestamp: datetime = field(default_factory=lambda: datetime.now(UTC))
+
+
+@dataclass
+class HistogramBucket:
+    """Histogram bucket for distribution metrics."""
+
+    le: float  # Less than or equal
+    count: int = 0
+
+
+class MemoryMetrics:
+    """
+    Collects memory system metrics.
+
+    Metrics tracked:
+    - Memory operations (get/set/delete by type and scope)
+    - Retrieval operations and latencies
+    - Memory item counts by type
+    - Consolidation operations and durations
+    - Cache hit/miss rates
+    - Procedure success rates
+    - Embedding operations
+    """
+
+    # Maximum samples to keep in histogram (circular buffer)
+    MAX_HISTOGRAM_SAMPLES = 10000
+
+    def __init__(self) -> None:
+        """Initialize MemoryMetrics."""
+        self._counters: dict[str, Counter[str]] = defaultdict(Counter)
+        self._gauges: dict[str, dict[str, float]] = defaultdict(dict)
+        # Use deque with maxlen for bounded memory (circular buffer)
+        self._histograms: dict[str, deque[float]] = defaultdict(
+            lambda: deque(maxlen=self.MAX_HISTOGRAM_SAMPLES)
+        )
+        self._histogram_buckets: dict[str, list[HistogramBucket]] = {}
+        self._lock = asyncio.Lock()
+
+        # Initialize histogram buckets
+        self._init_histogram_buckets()
+
+    def _init_histogram_buckets(self) -> None:
+        """Initialize histogram buckets for latency metrics."""
+        # Fast operations (working memory)
+        fast_buckets = [0.001, 0.005, 0.01, 0.025, 0.05, 0.1, 0.25, float("inf")]
+
+        # Normal operations (retrieval)
+        normal_buckets = [0.01, 0.025, 0.05, 0.1, 0.25, 0.5, 1.0, 2.5, float("inf")]
+
+        # Slow operations (consolidation)
+        slow_buckets = [0.1, 0.5, 1.0, 2.5, 5.0, 10.0, 30.0, 60.0, float("inf")]
+
+        self._histogram_buckets["memory_working_latency_seconds"] = [
+            HistogramBucket(le=b) for b in fast_buckets
+        ]
+        self._histogram_buckets["memory_retrieval_latency_seconds"] = [
+            HistogramBucket(le=b) for b in normal_buckets
+        ]
+        self._histogram_buckets["memory_consolidation_duration_seconds"] = [
+            HistogramBucket(le=b) for b in slow_buckets
+        ]
+        self._histogram_buckets["memory_embedding_latency_seconds"] = [
+            HistogramBucket(le=b) for b in normal_buckets
+        ]
+
+    # Counter methods - Operations
+
+    async def inc_operations(
+        self,
+        operation: str,
+        memory_type: str,
+        scope: str | None = None,
+        success: bool = True,
+    ) -> None:
+        """Increment memory operation counter."""
+        async with self._lock:
+            labels = f"operation={operation},memory_type={memory_type}"
+            if scope:
+                labels += f",scope={scope}"
+            labels += f",success={str(success).lower()}"
+            self._counters["memory_operations_total"][labels] += 1
+
+    async def inc_retrieval(
+        self,
+        memory_type: str,
+        strategy: str,
+        results_count: int,
+    ) -> None:
+        """Increment retrieval counter."""
+        async with self._lock:
+            labels = f"memory_type={memory_type},strategy={strategy}"
+            self._counters["memory_retrievals_total"][labels] += 1
+
+            # Track result counts as a separate metric
+            self._counters["memory_retrieval_results_total"][labels] += results_count
+
+    async def inc_cache_hit(self, cache_type: str) -> None:
+        """Increment cache hit counter."""
+        async with self._lock:
+            labels = f"cache_type={cache_type}"
+            self._counters["memory_cache_hits_total"][labels] += 1
+
+    async def inc_cache_miss(self, cache_type: str) -> None:
+        """Increment cache miss counter."""
+        async with self._lock:
+            labels = f"cache_type={cache_type}"
+            self._counters["memory_cache_misses_total"][labels] += 1
+
+    async def inc_consolidation(
+        self,
+        consolidation_type: str,
+        success: bool = True,
+    ) -> None:
+        """Increment consolidation counter."""
+        async with self._lock:
+            labels = f"type={consolidation_type},success={str(success).lower()}"
+            self._counters["memory_consolidations_total"][labels] += 1
+
+    async def inc_procedure_execution(
+        self,
+        procedure_id: str | None = None,
+        success: bool = True,
+    ) -> None:
+        """Increment procedure execution counter."""
+        async with self._lock:
+            labels = f"success={str(success).lower()}"
+            self._counters["memory_procedure_executions_total"][labels] += 1
+
+    async def inc_embeddings_generated(self, memory_type: str) -> None:
+        """Increment embeddings generated counter."""
+        async with self._lock:
+            labels = f"memory_type={memory_type}"
+            self._counters["memory_embeddings_generated_total"][labels] += 1
+
+    async def inc_fact_reinforcements(self) -> None:
+        """Increment fact reinforcement counter."""
+        async with self._lock:
+            self._counters["memory_fact_reinforcements_total"][""] += 1
+
+    async def inc_episodes_recorded(self, outcome: str) -> None:
+        """Increment episodes recorded counter."""
+        async with self._lock:
+            labels = f"outcome={outcome}"
+            self._counters["memory_episodes_recorded_total"][labels] += 1
+
+    async def inc_anomalies_detected(self, anomaly_type: str) -> None:
+        """Increment anomaly detection counter."""
+        async with self._lock:
+            labels = f"anomaly_type={anomaly_type}"
+            self._counters["memory_anomalies_detected_total"][labels] += 1
+
+    async def inc_patterns_detected(self, pattern_type: str) -> None:
+        """Increment pattern detection counter."""
+        async with self._lock:
+            labels = f"pattern_type={pattern_type}"
+            self._counters["memory_patterns_detected_total"][labels] += 1
+
+    async def inc_insights_generated(self, insight_type: str) -> None:
+        """Increment insight generation counter."""
+        async with self._lock:
+            labels = f"insight_type={insight_type}"
+            self._counters["memory_insights_generated_total"][labels] += 1
+
+    # Gauge methods
+
+    async def set_memory_items_count(
+        self,
+        memory_type: str,
+        scope: str,
+        count: int,
+    ) -> None:
+        """Set memory item count gauge."""
+        async with self._lock:
+            labels = f"memory_type={memory_type},scope={scope}"
+            self._gauges["memory_items_count"][labels] = float(count)
+
+    async def set_memory_size_bytes(
+        self,
+        memory_type: str,
+        scope: str,
+        size_bytes: int,
+    ) -> None:
+        """Set memory size gauge in bytes."""
+        async with self._lock:
+            labels = f"memory_type={memory_type},scope={scope}"
+            self._gauges["memory_size_bytes"][labels] = float(size_bytes)
+
+    async def set_cache_size(self, cache_type: str, size: int) -> None:
+        """Set cache size gauge."""
+        async with self._lock:
+            labels = f"cache_type={cache_type}"
+            self._gauges["memory_cache_size"][labels] = float(size)
+
+    async def set_procedure_success_rate(
+        self,
+        procedure_name: str,
+        rate: float,
+    ) -> None:
+        """Set procedure success rate gauge (0-1)."""
+        async with self._lock:
+            labels = f"procedure_name={procedure_name}"
+            self._gauges["memory_procedure_success_rate"][labels] = rate
+
+    async def set_active_sessions(self, count: int) -> None:
+        """Set active working memory sessions gauge."""
+        async with self._lock:
+            self._gauges["memory_active_sessions"][""] = float(count)
+
+    async def set_pending_consolidations(self, count: int) -> None:
+        """Set pending consolidations gauge."""
+        async with self._lock:
+            self._gauges["memory_pending_consolidations"][""] = float(count)
+
+    # Histogram methods
+
+    async def observe_working_latency(self, latency_seconds: float) -> None:
+        """Observe working memory operation latency."""
+        async with self._lock:
+            self._observe_histogram("memory_working_latency_seconds", latency_seconds)
+
+    async def observe_retrieval_latency(self, latency_seconds: float) -> None:
+        """Observe retrieval latency."""
+        async with self._lock:
+            self._observe_histogram("memory_retrieval_latency_seconds", latency_seconds)
+
+    async def observe_consolidation_duration(self, duration_seconds: float) -> None:
+        """Observe consolidation duration."""
+        async with self._lock:
+            self._observe_histogram(
+                "memory_consolidation_duration_seconds", duration_seconds
+            )
+
+    async def observe_embedding_latency(self, latency_seconds: float) -> None:
+        """Observe embedding generation latency."""
+        async with self._lock:
+            self._observe_histogram("memory_embedding_latency_seconds", latency_seconds)
+
+    def _observe_histogram(self, name: str, value: float) -> None:
+        """Record a value in a histogram."""
+        self._histograms[name].append(value)
+
+        # Update buckets
+        if name in self._histogram_buckets:
+            for bucket in self._histogram_buckets[name]:
+                if value <= bucket.le:
+                    bucket.count += 1
+
+    # Export methods
+
+    async def get_all_metrics(self) -> list[MetricValue]:
+        """Get all metrics as MetricValue objects."""
+        metrics: list[MetricValue] = []
+
+        async with self._lock:
+            # Export counters
+            for name, counter in self._counters.items():
+                for labels_str, value in counter.items():
+                    labels = self._parse_labels(labels_str)
+                    metrics.append(
+                        MetricValue(
+                            name=name,
+                            metric_type=MetricType.COUNTER,
+                            value=float(value),
+                            labels=labels,
+                        )
+                    )
+
+            # Export gauges
+            for name, gauge_dict in self._gauges.items():
+                for labels_str, gauge_value in gauge_dict.items():
+                    gauge_labels = self._parse_labels(labels_str)
+                    metrics.append(
+                        MetricValue(
+                            name=name,
+                            metric_type=MetricType.GAUGE,
+                            value=gauge_value,
+                            labels=gauge_labels,
+                        )
+                    )
+
+            # Export histogram summaries
+            for name, values in self._histograms.items():
+                if values:
+                    metrics.append(
+                        MetricValue(
+                            name=f"{name}_count",
+                            metric_type=MetricType.COUNTER,
+                            value=float(len(values)),
+                        )
+                    )
+                    metrics.append(
+                        MetricValue(
+                            name=f"{name}_sum",
+                            metric_type=MetricType.COUNTER,
+                            value=sum(values),
+                        )
+                    )
+
+        return metrics
+
+    async def get_prometheus_format(self) -> str:
+        """Export metrics in Prometheus text format."""
+        lines: list[str] = []
+
+        async with self._lock:
+            # Export counters
+            for name, counter in self._counters.items():
+                lines.append(f"# TYPE {name} counter")
+                for labels_str, value in counter.items():
+                    if labels_str:
+                        lines.append(f"{name}{{{labels_str}}} {value}")
+                    else:
+                        lines.append(f"{name} {value}")
+
+            # Export gauges
+            for name, gauge_dict in self._gauges.items():
+                lines.append(f"# TYPE {name} gauge")
+                for labels_str, gauge_value in gauge_dict.items():
+                    if labels_str:
+                        lines.append(f"{name}{{{labels_str}}} {gauge_value}")
+                    else:
+                        lines.append(f"{name} {gauge_value}")
+
+            # Export histograms
+            for name, buckets in self._histogram_buckets.items():
+                lines.append(f"# TYPE {name} histogram")
+                for bucket in buckets:
+                    le_str = "+Inf" if bucket.le == float("inf") else str(bucket.le)
+                    lines.append(f'{name}_bucket{{le="{le_str}"}} {bucket.count}')
+
+                if name in self._histograms:
+                    values = self._histograms[name]
+                    lines.append(f"{name}_count {len(values)}")
+                    lines.append(f"{name}_sum {sum(values)}")
+
+        return "\n".join(lines)
+
+    async def get_summary(self) -> dict[str, Any]:
+        """Get a summary of key metrics."""
+        async with self._lock:
+            total_operations = sum(self._counters["memory_operations_total"].values())
+            successful_operations = sum(
+                v
+                for k, v in self._counters["memory_operations_total"].items()
+                if "success=true" in k
+            )
+
+            total_retrievals = sum(self._counters["memory_retrievals_total"].values())
+
+            total_cache_hits = sum(self._counters["memory_cache_hits_total"].values())
+            total_cache_misses = sum(
+                self._counters["memory_cache_misses_total"].values()
+            )
+            cache_hit_rate = (
+                total_cache_hits / (total_cache_hits + total_cache_misses)
+                if (total_cache_hits + total_cache_misses) > 0
+                else 0.0
+            )
+
+            total_consolidations = sum(
+                self._counters["memory_consolidations_total"].values()
+            )
+
+            total_episodes = sum(
+                self._counters["memory_episodes_recorded_total"].values()
+            )
+
+            # Calculate average latencies
+            retrieval_latencies = list(
+                self._histograms.get("memory_retrieval_latency_seconds", deque())
+            )
+            avg_retrieval_latency = (
+                sum(retrieval_latencies) / len(retrieval_latencies)
+                if retrieval_latencies
+                else 0.0
+            )
+
+            return {
+                "total_operations": total_operations,
+                "successful_operations": successful_operations,
+                "operation_success_rate": (
+                    successful_operations / total_operations
+                    if total_operations > 0
+                    else 1.0
+                ),
+                "total_retrievals": total_retrievals,
+                "cache_hit_rate": cache_hit_rate,
+                "total_consolidations": total_consolidations,
+                "total_episodes_recorded": total_episodes,
+                "avg_retrieval_latency_ms": avg_retrieval_latency * 1000,
+                "patterns_detected": sum(
+                    self._counters["memory_patterns_detected_total"].values()
+                ),
+                "insights_generated": sum(
+                    self._counters["memory_insights_generated_total"].values()
+                ),
+                "anomalies_detected": sum(
+                    self._counters["memory_anomalies_detected_total"].values()
+                ),
+                "active_sessions": self._gauges.get("memory_active_sessions", {}).get(
+                    "", 0
+                ),
+                "pending_consolidations": self._gauges.get(
+                    "memory_pending_consolidations", {}
+                ).get("", 0),
+            }
+
+    async def get_cache_stats(self) -> dict[str, Any]:
+        """Get detailed cache statistics."""
+        async with self._lock:
+            stats: dict[str, Any] = {}
+
+            # Get hits/misses by cache type
+            for labels_str, hits in self._counters["memory_cache_hits_total"].items():
+                cache_type = self._parse_labels(labels_str).get("cache_type", "unknown")
+                if cache_type not in stats:
+                    stats[cache_type] = {"hits": 0, "misses": 0}
+                stats[cache_type]["hits"] = hits
+
+            for labels_str, misses in self._counters[
+                "memory_cache_misses_total"
+            ].items():
+                cache_type = self._parse_labels(labels_str).get("cache_type", "unknown")
+                if cache_type not in stats:
+                    stats[cache_type] = {"hits": 0, "misses": 0}
+                stats[cache_type]["misses"] = misses
+
+            # Calculate hit rates
+            for data in stats.values():
+                total = data["hits"] + data["misses"]
+                data["hit_rate"] = data["hits"] / total if total > 0 else 0.0
+                data["total"] = total
+
+            return stats
+
+    async def reset(self) -> None:
+        """Reset all metrics."""
+        async with self._lock:
+            self._counters.clear()
+            self._gauges.clear()
+            self._histograms.clear()
+            self._init_histogram_buckets()
+
+    def _parse_labels(self, labels_str: str) -> dict[str, str]:
+        """Parse labels string into dictionary."""
+        if not labels_str:
+            return {}
+
+        labels = {}
+        for pair in labels_str.split(","):
+            if "=" in pair:
+                key, value = pair.split("=", 1)
+                labels[key.strip()] = value.strip()
+
+        return labels
+
+
+# Singleton instance
+_metrics: MemoryMetrics | None = None
+_lock = asyncio.Lock()
+
+
+async def get_memory_metrics() -> MemoryMetrics:
+    """Get the singleton MemoryMetrics instance."""
+    global _metrics
+
+    async with _lock:
+        if _metrics is None:
+            _metrics = MemoryMetrics()
+        return _metrics
+
+
+async def reset_memory_metrics() -> None:
+    """Reset the singleton instance (for testing)."""
+    global _metrics
+    async with _lock:
+        _metrics = None
+
+
+# Convenience functions
+
+
+async def record_memory_operation(
+    operation: str,
+    memory_type: str,
+    scope: str | None = None,
+    success: bool = True,
+    latency_ms: float | None = None,
+) -> None:
+    """Record a memory operation."""
+    metrics = await get_memory_metrics()
+    await metrics.inc_operations(operation, memory_type, scope, success)
+
+    if latency_ms is not None and memory_type == "working":
+        await metrics.observe_working_latency(latency_ms / 1000)
+
+
+async def record_retrieval(
+    memory_type: str,
+    strategy: str,
+    results_count: int,
+    latency_ms: float,
+) -> None:
+    """Record a retrieval operation."""
+    metrics = await get_memory_metrics()
+    await metrics.inc_retrieval(memory_type, strategy, results_count)
+    await metrics.observe_retrieval_latency(latency_ms / 1000)

backend/app/services/memory/procedural/__init__.py

@@ -0,0 +1,22 @@
+# app/services/memory/procedural/__init__.py
+"""
+Procedural Memory
+
+Learned skills and procedures from successful task patterns.
+"""
+
+from .matching import (
+    MatchContext,
+    MatchResult,
+    ProcedureMatcher,
+    get_procedure_matcher,
+)
+from .memory import ProceduralMemory
+
+__all__ = [
+    "MatchContext",
+    "MatchResult",
+    "ProceduralMemory",
+    "ProcedureMatcher",
+    "get_procedure_matcher",
+]

backend/app/services/memory/procedural/matching.py

@@ -0,0 +1,291 @@
+# app/services/memory/procedural/matching.py
+"""
+Procedure Matching.
+
+Provides utilities for matching procedures to contexts,
+ranking procedures by relevance, and suggesting procedures.
+"""
+
+import logging
+import re
+from dataclasses import dataclass, field
+from typing import Any, ClassVar
+
+from app.services.memory.types import Procedure
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class MatchResult:
+    """Result of a procedure match."""
+
+    procedure: Procedure
+    score: float
+    matched_terms: list[str] = field(default_factory=list)
+    match_type: str = "keyword"  # keyword, semantic, pattern
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "procedure_id": str(self.procedure.id),
+            "procedure_name": self.procedure.name,
+            "score": self.score,
+            "matched_terms": self.matched_terms,
+            "match_type": self.match_type,
+            "success_rate": self.procedure.success_rate,
+        }
+
+
+@dataclass
+class MatchContext:
+    """Context for procedure matching."""
+
+    query: str
+    task_type: str | None = None
+    project_id: Any | None = None
+    agent_type_id: Any | None = None
+    max_results: int = 5
+    min_score: float = 0.3
+    require_success_rate: float | None = None
+
+
+class ProcedureMatcher:
+    """
+    Matches procedures to contexts using multiple strategies.
+
+    Matching strategies:
+    - Keyword matching on trigger pattern and name
+    - Pattern-based matching using regex
+    - Success rate weighting
+
+    In production, this would be augmented with vector similarity search.
+    """
+
+    # Common task-related keywords for boosting
+    TASK_KEYWORDS: ClassVar[set[str]] = {
+        "create",
+        "update",
+        "delete",
+        "fix",
+        "implement",
+        "add",
+        "remove",
+        "refactor",
+        "test",
+        "deploy",
+        "configure",
+        "setup",
+        "build",
+        "debug",
+        "optimize",
+    }
+
+    def __init__(self) -> None:
+        """Initialize the matcher."""
+        self._compiled_patterns: dict[str, re.Pattern[str]] = {}
+
+    def match(
+        self,
+        procedures: list[Procedure],
+        context: MatchContext,
+    ) -> list[MatchResult]:
+        """
+        Match procedures against a context.
+
+        Args:
+            procedures: List of procedures to match
+            context: Matching context
+
+        Returns:
+            List of match results, sorted by score (highest first)
+        """
+        results: list[MatchResult] = []
+
+        query_terms = self._extract_terms(context.query)
+        query_lower = context.query.lower()
+
+        for procedure in procedures:
+            score, matched = self._calculate_match_score(
+                procedure=procedure,
+                query_terms=query_terms,
+                query_lower=query_lower,
+                context=context,
+            )
+
+            if score >= context.min_score:
+                # Apply success rate boost
+                if context.require_success_rate is not None:
+                    if procedure.success_rate < context.require_success_rate:
+                        continue
+
+                # Boost score based on success rate
+                success_boost = procedure.success_rate * 0.2
+                final_score = min(1.0, score + success_boost)
+
+                results.append(
+                    MatchResult(
+                        procedure=procedure,
+                        score=final_score,
+                        matched_terms=matched,
+                        match_type="keyword",
+                    )
+                )
+
+        # Sort by score descending
+        results.sort(key=lambda r: r.score, reverse=True)
+
+        return results[: context.max_results]
+
+    def _extract_terms(self, text: str) -> list[str]:
+        """Extract searchable terms from text."""
+        # Remove special characters and split
+        clean = re.sub(r"[^\w\s-]", " ", text.lower())
+        terms = clean.split()
+
+        # Filter out very short terms
+        return [t for t in terms if len(t) >= 2]
+
+    def _calculate_match_score(
+        self,
+        procedure: Procedure,
+        query_terms: list[str],
+        query_lower: str,
+        context: MatchContext,
+    ) -> tuple[float, list[str]]:
+        """
+        Calculate match score between procedure and query.
+
+        Returns:
+            Tuple of (score, matched_terms)
+        """
+        score = 0.0
+        matched: list[str] = []
+
+        trigger_lower = procedure.trigger_pattern.lower()
+        name_lower = procedure.name.lower()
+
+        # Exact name match - high score
+        if name_lower in query_lower or query_lower in name_lower:
+            score += 0.5
+            matched.append(f"name:{procedure.name}")
+
+        # Trigger pattern match
+        if trigger_lower in query_lower or query_lower in trigger_lower:
+            score += 0.4
+            matched.append(f"trigger:{procedure.trigger_pattern[:30]}")
+
+        # Term-by-term matching
+        for term in query_terms:
+            if term in trigger_lower:
+                score += 0.1
+                matched.append(term)
+            elif term in name_lower:
+                score += 0.08
+                matched.append(term)
+
+            # Boost for task keywords
+            if term in self.TASK_KEYWORDS:
+                if term in trigger_lower or term in name_lower:
+                    score += 0.05
+
+        # Task type match if provided
+        if context.task_type:
+            task_type_lower = context.task_type.lower()
+            if task_type_lower in trigger_lower or task_type_lower in name_lower:
+                score += 0.3
+                matched.append(f"task_type:{context.task_type}")
+
+        # Regex pattern matching on trigger
+        try:
+            pattern = self._get_or_compile_pattern(trigger_lower)
+            if pattern and pattern.search(query_lower):
+                score += 0.25
+                matched.append("pattern_match")
+        except re.error:
+            pass  # Invalid regex, skip pattern matching
+
+        return min(1.0, score), matched
+
+    def _get_or_compile_pattern(self, pattern: str) -> re.Pattern[str] | None:
+        """Get or compile a regex pattern with caching."""
+        if pattern in self._compiled_patterns:
+            return self._compiled_patterns[pattern]
+
+        # Only compile if it looks like a regex pattern
+        if not any(c in pattern for c in r"\.*+?[]{}|()^$"):
+            return None
+
+        try:
+            compiled = re.compile(pattern, re.IGNORECASE)
+            self._compiled_patterns[pattern] = compiled
+            return compiled
+        except re.error:
+            return None
+
+    def rank_by_relevance(
+        self,
+        procedures: list[Procedure],
+        task_type: str,
+    ) -> list[Procedure]:
+        """
+        Rank procedures by relevance to a task type.
+
+        Args:
+            procedures: Procedures to rank
+            task_type: Task type for relevance
+
+        Returns:
+            Procedures sorted by relevance
+        """
+        context = MatchContext(
+            query=task_type,
+            task_type=task_type,
+            min_score=0.0,
+            max_results=len(procedures),
+        )
+
+        results = self.match(procedures, context)
+        return [r.procedure for r in results]
+
+    def suggest_procedures(
+        self,
+        procedures: list[Procedure],
+        query: str,
+        min_success_rate: float = 0.5,
+        max_suggestions: int = 3,
+    ) -> list[MatchResult]:
+        """
+        Suggest the best procedures for a query.
+
+        Only suggests procedures with sufficient success rate.
+
+        Args:
+            procedures: Available procedures
+            query: Query/context
+            min_success_rate: Minimum success rate to suggest
+            max_suggestions: Maximum suggestions
+
+        Returns:
+            List of procedure suggestions
+        """
+        context = MatchContext(
+            query=query,
+            max_results=max_suggestions,
+            min_score=0.2,
+            require_success_rate=min_success_rate,
+        )
+
+        return self.match(procedures, context)
+
+
+# Singleton matcher instance
+_matcher: ProcedureMatcher | None = None
+
+
+def get_procedure_matcher() -> ProcedureMatcher:
+    """Get the singleton procedure matcher instance."""
+    global _matcher
+    if _matcher is None:
+        _matcher = ProcedureMatcher()
+    return _matcher

backend/app/services/memory/procedural/memory.py

@@ -0,0 +1,749 @@
+# app/services/memory/procedural/memory.py
+"""
+Procedural Memory Implementation.
+
+Provides storage and retrieval for learned procedures (skills)
+derived from successful task execution patterns.
+"""
+
+import logging
+import time
+from datetime import UTC, datetime
+from typing import Any
+from uuid import UUID
+
+from sqlalchemy import and_, desc, or_, select, update
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.memory.procedure import Procedure as ProcedureModel
+from app.services.memory.config import get_memory_settings
+from app.services.memory.types import Procedure, ProcedureCreate, RetrievalResult, Step
+
+logger = logging.getLogger(__name__)
+
+
+def _escape_like_pattern(pattern: str) -> str:
+    """
+    Escape SQL LIKE/ILIKE special characters to prevent pattern injection.
+
+    Characters escaped:
+    - % (matches zero or more characters)
+    - _ (matches exactly one character)
+    - \\ (escape character itself)
+
+    Args:
+        pattern: Raw search pattern from user input
+
+    Returns:
+        Escaped pattern safe for use in LIKE/ILIKE queries
+    """
+    # Escape backslash first, then the wildcards
+    return pattern.replace("\\", "\\\\").replace("%", "\\%").replace("_", "\\_")
+
+
+def _model_to_procedure(model: ProcedureModel) -> Procedure:
+    """Convert SQLAlchemy model to Procedure dataclass."""
+    return Procedure(
+        id=model.id,  # type: ignore[arg-type]
+        project_id=model.project_id,  # type: ignore[arg-type]
+        agent_type_id=model.agent_type_id,  # type: ignore[arg-type]
+        name=model.name,  # type: ignore[arg-type]
+        trigger_pattern=model.trigger_pattern,  # type: ignore[arg-type]
+        steps=model.steps or [],  # type: ignore[arg-type]
+        success_count=model.success_count,  # type: ignore[arg-type]
+        failure_count=model.failure_count,  # type: ignore[arg-type]
+        last_used=model.last_used,  # type: ignore[arg-type]
+        embedding=None,  # Don't expose raw embedding
+        created_at=model.created_at,  # type: ignore[arg-type]
+        updated_at=model.updated_at,  # type: ignore[arg-type]
+    )
+
+
+class ProceduralMemory:
+    """
+    Procedural Memory Service.
+
+    Provides procedure storage and retrieval:
+    - Record procedures from successful task patterns
+    - Find matching procedures by trigger pattern
+    - Track success/failure rates
+    - Get best procedure for a task type
+    - Update procedure steps
+
+    Performance target: <50ms P95 for matching
+    """
+
+    def __init__(
+        self,
+        session: AsyncSession,
+        embedding_generator: Any | None = None,
+    ) -> None:
+        """
+        Initialize procedural memory.
+
+        Args:
+            session: Database session
+            embedding_generator: Optional embedding generator for semantic matching
+        """
+        self._session = session
+        self._embedding_generator = embedding_generator
+        self._settings = get_memory_settings()
+
+    @classmethod
+    async def create(
+        cls,
+        session: AsyncSession,
+        embedding_generator: Any | None = None,
+    ) -> "ProceduralMemory":
+        """
+        Factory method to create ProceduralMemory.
+
+        Args:
+            session: Database session
+            embedding_generator: Optional embedding generator
+
+        Returns:
+            Configured ProceduralMemory instance
+        """
+        return cls(session=session, embedding_generator=embedding_generator)
+
+    # =========================================================================
+    # Procedure Recording
+    # =========================================================================
+
+    async def record_procedure(self, procedure: ProcedureCreate) -> Procedure:
+        """
+        Record a new procedure or update an existing one.
+
+        If a procedure with the same name exists in the same scope,
+        its steps will be updated and success count incremented.
+
+        Args:
+            procedure: Procedure data to record
+
+        Returns:
+            The created or updated procedure
+        """
+        # Check for existing procedure with same name
+        existing = await self._find_existing_procedure(
+            project_id=procedure.project_id,
+            agent_type_id=procedure.agent_type_id,
+            name=procedure.name,
+        )
+
+        if existing is not None:
+            # Update existing procedure
+            return await self._update_existing_procedure(
+                existing=existing,
+                new_steps=procedure.steps,
+                new_trigger=procedure.trigger_pattern,
+            )
+
+        # Create new procedure
+        now = datetime.now(UTC)
+
+        # Generate embedding if possible
+        embedding = None
+        if self._embedding_generator is not None:
+            embedding_text = self._create_embedding_text(procedure)
+            embedding = await self._embedding_generator.generate(embedding_text)
+
+        model = ProcedureModel(
+            project_id=procedure.project_id,
+            agent_type_id=procedure.agent_type_id,
+            name=procedure.name,
+            trigger_pattern=procedure.trigger_pattern,
+            steps=procedure.steps,
+            success_count=1,  # New procedures start with 1 success (they worked)
+            failure_count=0,
+            last_used=now,
+            embedding=embedding,
+        )
+
+        self._session.add(model)
+        await self._session.flush()
+        await self._session.refresh(model)
+
+        logger.info(
+            f"Recorded new procedure: {procedure.name} with {len(procedure.steps)} steps"
+        )
+
+        return _model_to_procedure(model)
+
+    async def _find_existing_procedure(
+        self,
+        project_id: UUID | None,
+        agent_type_id: UUID | None,
+        name: str,
+    ) -> ProcedureModel | None:
+        """Find an existing procedure with the same name in the same scope."""
+        query = select(ProcedureModel).where(ProcedureModel.name == name)
+
+        if project_id is not None:
+            query = query.where(ProcedureModel.project_id == project_id)
+        else:
+            query = query.where(ProcedureModel.project_id.is_(None))
+
+        if agent_type_id is not None:
+            query = query.where(ProcedureModel.agent_type_id == agent_type_id)
+        else:
+            query = query.where(ProcedureModel.agent_type_id.is_(None))
+
+        result = await self._session.execute(query)
+        return result.scalar_one_or_none()
+
+    async def _update_existing_procedure(
+        self,
+        existing: ProcedureModel,
+        new_steps: list[dict[str, Any]],
+        new_trigger: str,
+    ) -> Procedure:
+        """Update an existing procedure with new steps."""
+        now = datetime.now(UTC)
+
+        # Merge steps intelligently - keep existing order, add new steps
+        merged_steps = self._merge_steps(
+            existing.steps or [],  # type: ignore[arg-type]
+            new_steps,
+        )
+
+        stmt = (
+            update(ProcedureModel)
+            .where(ProcedureModel.id == existing.id)
+            .values(
+                steps=merged_steps,
+                trigger_pattern=new_trigger,
+                success_count=ProcedureModel.success_count + 1,
+                last_used=now,
+                updated_at=now,
+            )
+            .returning(ProcedureModel)
+        )
+
+        result = await self._session.execute(stmt)
+        updated_model = result.scalar_one()
+        await self._session.flush()
+
+        logger.info(f"Updated existing procedure: {existing.name}")
+
+        return _model_to_procedure(updated_model)
+
+    def _merge_steps(
+        self,
+        existing_steps: list[dict[str, Any]],
+        new_steps: list[dict[str, Any]],
+    ) -> list[dict[str, Any]]:
+        """Merge steps from a new execution with existing steps."""
+        if not existing_steps:
+            return new_steps
+        if not new_steps:
+            return existing_steps
+
+        # For now, use the new steps if they differ significantly
+        # In production, this could use more sophisticated merging
+        if len(new_steps) != len(existing_steps):
+            # If structure changed, prefer newer steps
+            return new_steps
+
+        # Merge step-by-step, preferring new data where available
+        merged = []
+        for i, new_step in enumerate(new_steps):
+            if i < len(existing_steps):
+                # Merge with existing step
+                step = {**existing_steps[i], **new_step}
+            else:
+                step = new_step
+            merged.append(step)
+
+        return merged
+
+    def _create_embedding_text(self, procedure: ProcedureCreate) -> str:
+        """Create text for embedding from procedure data."""
+        steps_text = " ".join(step.get("action", "") for step in procedure.steps)
+        return f"{procedure.name} {procedure.trigger_pattern} {steps_text}"
+
+    # =========================================================================
+    # Procedure Retrieval
+    # =========================================================================
+
+    async def find_matching(
+        self,
+        context: str,
+        project_id: UUID | None = None,
+        agent_type_id: UUID | None = None,
+        limit: int = 5,
+    ) -> list[Procedure]:
+        """
+        Find procedures matching the given context.
+
+        Args:
+            context: Context/trigger to match against
+            project_id: Optional project to search within
+            agent_type_id: Optional agent type filter
+            limit: Maximum results
+
+        Returns:
+            List of matching procedures
+        """
+        result = await self._find_matching_with_metadata(
+            context=context,
+            project_id=project_id,
+            agent_type_id=agent_type_id,
+            limit=limit,
+        )
+        return result.items
+
+    async def _find_matching_with_metadata(
+        self,
+        context: str,
+        project_id: UUID | None = None,
+        agent_type_id: UUID | None = None,
+        limit: int = 5,
+    ) -> RetrievalResult[Procedure]:
+        """Find matching procedures with full result metadata."""
+        start_time = time.perf_counter()
+
+        # Build base query - prioritize by success rate
+        stmt = (
+            select(ProcedureModel)
+            .order_by(
+                desc(
+                    ProcedureModel.success_count
+                    / (ProcedureModel.success_count + ProcedureModel.failure_count + 1)
+                ),
+                desc(ProcedureModel.last_used),
+            )
+            .limit(limit)
+        )
+
+        # Apply scope filters
+        if project_id is not None:
+            stmt = stmt.where(
+                or_(
+                    ProcedureModel.project_id == project_id,
+                    ProcedureModel.project_id.is_(None),
+                )
+            )
+
+        if agent_type_id is not None:
+            stmt = stmt.where(
+                or_(
+                    ProcedureModel.agent_type_id == agent_type_id,
+                    ProcedureModel.agent_type_id.is_(None),
+                )
+            )
+
+        # Text-based matching on trigger pattern and name
+        # TODO: Implement proper vector similarity search when pgvector is integrated
+        search_terms = context.lower().split()[:5]  # Limit to 5 terms
+        if search_terms:
+            conditions = []
+            for term in search_terms:
+                # Escape SQL wildcards to prevent pattern injection
+                escaped_term = _escape_like_pattern(term)
+                term_pattern = f"%{escaped_term}%"
+                conditions.append(
+                    or_(
+                        ProcedureModel.trigger_pattern.ilike(term_pattern),
+                        ProcedureModel.name.ilike(term_pattern),
+                    )
+                )
+            if conditions:
+                stmt = stmt.where(or_(*conditions))
+
+        result = await self._session.execute(stmt)
+        models = list(result.scalars().all())
+
+        latency_ms = (time.perf_counter() - start_time) * 1000
+
+        return RetrievalResult(
+            items=[_model_to_procedure(m) for m in models],
+            total_count=len(models),
+            query=context,
+            retrieval_type="procedural",
+            latency_ms=latency_ms,
+            metadata={"project_id": str(project_id) if project_id else None},
+        )
+
+    async def get_best_procedure(
+        self,
+        task_type: str,
+        project_id: UUID | None = None,
+        agent_type_id: UUID | None = None,
+        min_success_rate: float = 0.5,
+        min_uses: int = 1,
+    ) -> Procedure | None:
+        """
+        Get the best procedure for a given task type.
+
+        Returns the procedure with the highest success rate that
+        meets the minimum thresholds.
+
+        Args:
+            task_type: Task type to find procedure for
+            project_id: Optional project scope
+            agent_type_id: Optional agent type scope
+            min_success_rate: Minimum required success rate
+            min_uses: Minimum number of uses required
+
+        Returns:
+            Best matching procedure or None
+        """
+        # Escape SQL wildcards to prevent pattern injection
+        escaped_task_type = _escape_like_pattern(task_type)
+        task_type_pattern = f"%{escaped_task_type}%"
+
+        # Build query for procedures matching task type
+        stmt = (
+            select(ProcedureModel)
+            .where(
+                and_(
+                    (ProcedureModel.success_count + ProcedureModel.failure_count)
+                    >= min_uses,
+                    or_(
+                        ProcedureModel.trigger_pattern.ilike(task_type_pattern),
+                        ProcedureModel.name.ilike(task_type_pattern),
+                    ),
+                )
+            )
+            .order_by(
+                desc(
+                    ProcedureModel.success_count
+                    / (ProcedureModel.success_count + ProcedureModel.failure_count + 1)
+                ),
+                desc(ProcedureModel.last_used),
+            )
+            .limit(10)
+        )
+
+        # Apply scope filters
+        if project_id is not None:
+            stmt = stmt.where(
+                or_(
+                    ProcedureModel.project_id == project_id,
+                    ProcedureModel.project_id.is_(None),
+                )
+            )
+
+        if agent_type_id is not None:
+            stmt = stmt.where(
+                or_(
+                    ProcedureModel.agent_type_id == agent_type_id,
+                    ProcedureModel.agent_type_id.is_(None),
+                )
+            )
+
+        result = await self._session.execute(stmt)
+        models = list(result.scalars().all())
+
+        # Filter by success rate in Python (SQLAlchemy division in WHERE is complex)
+        for model in models:
+            success = float(model.success_count)
+            failure = float(model.failure_count)
+            total = success + failure
+            if total > 0 and (success / total) >= min_success_rate:
+                logger.debug(
+                    f"Found best procedure for '{task_type}': {model.name} "
+                    f"(success_rate={success / total:.2%})"
+                )
+                return _model_to_procedure(model)
+
+        return None
+
+    async def get_by_id(self, procedure_id: UUID) -> Procedure | None:
+        """Get a procedure by ID."""
+        query = select(ProcedureModel).where(ProcedureModel.id == procedure_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+        return _model_to_procedure(model) if model else None
+
+    # =========================================================================
+    # Outcome Recording
+    # =========================================================================
+
+    async def record_outcome(
+        self,
+        procedure_id: UUID,
+        success: bool,
+    ) -> Procedure:
+        """
+        Record the outcome of using a procedure.
+
+        Updates the success or failure count and last_used timestamp.
+
+        Args:
+            procedure_id: Procedure that was used
+            success: Whether the procedure succeeded
+
+        Returns:
+            Updated procedure
+
+        Raises:
+            ValueError: If procedure not found
+        """
+        query = select(ProcedureModel).where(ProcedureModel.id == procedure_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+
+        if model is None:
+            raise ValueError(f"Procedure not found: {procedure_id}")
+
+        now = datetime.now(UTC)
+
+        if success:
+            stmt = (
+                update(ProcedureModel)
+                .where(ProcedureModel.id == procedure_id)
+                .values(
+                    success_count=ProcedureModel.success_count + 1,
+                    last_used=now,
+                    updated_at=now,
+                )
+                .returning(ProcedureModel)
+            )
+        else:
+            stmt = (
+                update(ProcedureModel)
+                .where(ProcedureModel.id == procedure_id)
+                .values(
+                    failure_count=ProcedureModel.failure_count + 1,
+                    last_used=now,
+                    updated_at=now,
+                )
+                .returning(ProcedureModel)
+            )
+
+        result = await self._session.execute(stmt)
+        updated_model = result.scalar_one()
+        await self._session.flush()
+
+        outcome = "success" if success else "failure"
+        logger.info(
+            f"Recorded {outcome} for procedure {procedure_id}: "
+            f"success_rate={updated_model.success_rate:.2%}"
+        )
+
+        return _model_to_procedure(updated_model)
+
+    # =========================================================================
+    # Step Management
+    # =========================================================================
+
+    async def update_steps(
+        self,
+        procedure_id: UUID,
+        steps: list[Step],
+    ) -> Procedure:
+        """
+        Update the steps of a procedure.
+
+        Args:
+            procedure_id: Procedure to update
+            steps: New steps
+
+        Returns:
+            Updated procedure
+
+        Raises:
+            ValueError: If procedure not found
+        """
+        query = select(ProcedureModel).where(ProcedureModel.id == procedure_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+
+        if model is None:
+            raise ValueError(f"Procedure not found: {procedure_id}")
+
+        # Convert Step objects to dictionaries
+        steps_dict = [
+            {
+                "order": step.order,
+                "action": step.action,
+                "parameters": step.parameters,
+                "expected_outcome": step.expected_outcome,
+                "fallback_action": step.fallback_action,
+            }
+            for step in steps
+        ]
+
+        now = datetime.now(UTC)
+        stmt = (
+            update(ProcedureModel)
+            .where(ProcedureModel.id == procedure_id)
+            .values(
+                steps=steps_dict,
+                updated_at=now,
+            )
+            .returning(ProcedureModel)
+        )
+
+        result = await self._session.execute(stmt)
+        updated_model = result.scalar_one()
+        await self._session.flush()
+
+        logger.info(f"Updated steps for procedure {procedure_id}: {len(steps)} steps")
+
+        return _model_to_procedure(updated_model)
+
+    # =========================================================================
+    # Statistics & Management
+    # =========================================================================
+
+    async def get_stats(
+        self,
+        project_id: UUID | None = None,
+        agent_type_id: UUID | None = None,
+    ) -> dict[str, Any]:
+        """
+        Get statistics about procedural memory.
+
+        Args:
+            project_id: Optional project to get stats for
+            agent_type_id: Optional agent type filter
+
+        Returns:
+            Dictionary with statistics
+        """
+        query = select(ProcedureModel)
+
+        if project_id is not None:
+            query = query.where(
+                or_(
+                    ProcedureModel.project_id == project_id,
+                    ProcedureModel.project_id.is_(None),
+                )
+            )
+
+        if agent_type_id is not None:
+            query = query.where(
+                or_(
+                    ProcedureModel.agent_type_id == agent_type_id,
+                    ProcedureModel.agent_type_id.is_(None),
+                )
+            )
+
+        result = await self._session.execute(query)
+        models = list(result.scalars().all())
+
+        if not models:
+            return {
+                "total_procedures": 0,
+                "avg_success_rate": 0.0,
+                "avg_steps_count": 0.0,
+                "total_uses": 0,
+                "high_success_count": 0,
+                "low_success_count": 0,
+            }
+
+        success_rates = [m.success_rate for m in models]
+        step_counts = [len(m.steps or []) for m in models]
+        total_uses = sum(m.total_uses for m in models)
+
+        return {
+            "total_procedures": len(models),
+            "avg_success_rate": sum(success_rates) / len(success_rates),
+            "avg_steps_count": sum(step_counts) / len(step_counts),
+            "total_uses": total_uses,
+            "high_success_count": sum(1 for r in success_rates if r >= 0.8),
+            "low_success_count": sum(1 for r in success_rates if r < 0.5),
+        }
+
+    async def count(
+        self,
+        project_id: UUID | None = None,
+        agent_type_id: UUID | None = None,
+    ) -> int:
+        """
+        Count procedures in scope.
+
+        Args:
+            project_id: Optional project to count for
+            agent_type_id: Optional agent type filter
+
+        Returns:
+            Number of procedures
+        """
+        query = select(ProcedureModel)
+
+        if project_id is not None:
+            query = query.where(
+                or_(
+                    ProcedureModel.project_id == project_id,
+                    ProcedureModel.project_id.is_(None),
+                )
+            )
+
+        if agent_type_id is not None:
+            query = query.where(
+                or_(
+                    ProcedureModel.agent_type_id == agent_type_id,
+                    ProcedureModel.agent_type_id.is_(None),
+                )
+            )
+
+        result = await self._session.execute(query)
+        return len(list(result.scalars().all()))
+
+    async def delete(self, procedure_id: UUID) -> bool:
+        """
+        Delete a procedure.
+
+        Args:
+            procedure_id: Procedure to delete
+
+        Returns:
+            True if deleted, False if not found
+        """
+        query = select(ProcedureModel).where(ProcedureModel.id == procedure_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+
+        if model is None:
+            return False
+
+        await self._session.delete(model)
+        await self._session.flush()
+
+        logger.info(f"Deleted procedure {procedure_id}")
+        return True
+
+    async def get_procedures_by_success_rate(
+        self,
+        min_rate: float = 0.0,
+        max_rate: float = 1.0,
+        project_id: UUID | None = None,
+        limit: int = 20,
+    ) -> list[Procedure]:
+        """
+        Get procedures within a success rate range.
+
+        Args:
+            min_rate: Minimum success rate
+            max_rate: Maximum success rate
+            project_id: Optional project scope
+            limit: Maximum results
+
+        Returns:
+            List of procedures
+        """
+        query = (
+            select(ProcedureModel)
+            .order_by(desc(ProcedureModel.last_used))
+            .limit(limit * 2)  # Fetch more since we filter in Python
+        )
+
+        if project_id is not None:
+            query = query.where(
+                or_(
+                    ProcedureModel.project_id == project_id,
+                    ProcedureModel.project_id.is_(None),
+                )
+            )
+
+        result = await self._session.execute(query)
+        models = list(result.scalars().all())
+
+        # Filter by success rate in Python
+        filtered = [m for m in models if min_rate <= m.success_rate <= max_rate][:limit]
+
+        return [_model_to_procedure(m) for m in filtered]

backend/app/services/memory/reflection/__init__.py

@@ -0,0 +1,38 @@
+# app/services/memory/reflection/__init__.py
+"""
+Memory Reflection Layer.
+
+Analyzes patterns in agent experiences to generate actionable insights.
+"""
+
+from .service import (
+    MemoryReflection,
+    ReflectionConfig,
+    get_memory_reflection,
+)
+from .types import (
+    Anomaly,
+    AnomalyType,
+    Factor,
+    FactorType,
+    Insight,
+    InsightType,
+    Pattern,
+    PatternType,
+    TimeRange,
+)
+
+__all__ = [
+    "Anomaly",
+    "AnomalyType",
+    "Factor",
+    "FactorType",
+    "Insight",
+    "InsightType",
+    "MemoryReflection",
+    "Pattern",
+    "PatternType",
+    "ReflectionConfig",
+    "TimeRange",
+    "get_memory_reflection",
+]

backend/app/services/memory/reflection/types.py

@@ -0,0 +1,304 @@
+# app/services/memory/reflection/types.py
+"""
+Memory Reflection Types.
+
+Type definitions for pattern detection, anomaly detection, and insights.
+"""
+
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from enum import Enum
+from typing import Any
+from uuid import UUID
+
+
+def _utcnow() -> datetime:
+    """Get current UTC time as timezone-aware datetime."""
+    return datetime.now(UTC)
+
+
+class PatternType(str, Enum):
+    """Types of patterns detected in episodic memory."""
+
+    RECURRING_SUCCESS = "recurring_success"
+    RECURRING_FAILURE = "recurring_failure"
+    ACTION_SEQUENCE = "action_sequence"
+    CONTEXT_CORRELATION = "context_correlation"
+    TEMPORAL = "temporal"
+    EFFICIENCY = "efficiency"
+
+
+class FactorType(str, Enum):
+    """Types of factors contributing to outcomes."""
+
+    ACTION = "action"
+    CONTEXT = "context"
+    TIMING = "timing"
+    RESOURCE = "resource"
+    PRECEDING_STATE = "preceding_state"
+
+
+class AnomalyType(str, Enum):
+    """Types of anomalies detected."""
+
+    UNUSUAL_DURATION = "unusual_duration"
+    UNEXPECTED_OUTCOME = "unexpected_outcome"
+    UNUSUAL_TOKEN_USAGE = "unusual_token_usage"
+    UNUSUAL_FAILURE_RATE = "unusual_failure_rate"
+    UNUSUAL_ACTION_PATTERN = "unusual_action_pattern"
+
+
+class InsightType(str, Enum):
+    """Types of insights generated."""
+
+    OPTIMIZATION = "optimization"
+    WARNING = "warning"
+    LEARNING = "learning"
+    RECOMMENDATION = "recommendation"
+    TREND = "trend"
+
+
+@dataclass
+class TimeRange:
+    """Time range for reflection analysis."""
+
+    start: datetime
+    end: datetime
+
+    @classmethod
+    def last_hours(cls, hours: int = 24) -> "TimeRange":
+        """Create time range for last N hours."""
+        end = _utcnow()
+        start = datetime(
+            end.year, end.month, end.day, end.hour, end.minute, end.second, tzinfo=UTC
+        ) - __import__("datetime").timedelta(hours=hours)
+        return cls(start=start, end=end)
+
+    @classmethod
+    def last_days(cls, days: int = 7) -> "TimeRange":
+        """Create time range for last N days."""
+        from datetime import timedelta
+
+        end = _utcnow()
+        start = end - timedelta(days=days)
+        return cls(start=start, end=end)
+
+    @property
+    def duration_hours(self) -> float:
+        """Get duration in hours."""
+        return (self.end - self.start).total_seconds() / 3600
+
+    @property
+    def duration_days(self) -> float:
+        """Get duration in days."""
+        return (self.end - self.start).total_seconds() / 86400
+
+
+@dataclass
+class Pattern:
+    """A detected pattern in episodic memory."""
+
+    id: UUID
+    pattern_type: PatternType
+    name: str
+    description: str
+    confidence: float
+    occurrence_count: int
+    episode_ids: list[UUID]
+    first_seen: datetime
+    last_seen: datetime
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def frequency(self) -> float:
+        """Calculate pattern frequency per day."""
+        duration_days = (self.last_seen - self.first_seen).total_seconds() / 86400
+        if duration_days < 1:
+            duration_days = 1
+        return self.occurrence_count / duration_days
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "id": str(self.id),
+            "pattern_type": self.pattern_type.value,
+            "name": self.name,
+            "description": self.description,
+            "confidence": self.confidence,
+            "occurrence_count": self.occurrence_count,
+            "episode_ids": [str(eid) for eid in self.episode_ids],
+            "first_seen": self.first_seen.isoformat(),
+            "last_seen": self.last_seen.isoformat(),
+            "frequency": self.frequency,
+            "metadata": self.metadata,
+        }
+
+
+@dataclass
+class Factor:
+    """A factor contributing to success or failure."""
+
+    id: UUID
+    factor_type: FactorType
+    name: str
+    description: str
+    impact_score: float
+    correlation: float
+    sample_size: int
+    positive_examples: list[UUID]
+    negative_examples: list[UUID]
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def net_impact(self) -> float:
+        """Calculate net impact considering sample size."""
+        # Weight impact by sample confidence
+        confidence_weight = min(1.0, self.sample_size / 20)
+        return self.impact_score * self.correlation * confidence_weight
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "id": str(self.id),
+            "factor_type": self.factor_type.value,
+            "name": self.name,
+            "description": self.description,
+            "impact_score": self.impact_score,
+            "correlation": self.correlation,
+            "sample_size": self.sample_size,
+            "positive_examples": [str(eid) for eid in self.positive_examples],
+            "negative_examples": [str(eid) for eid in self.negative_examples],
+            "net_impact": self.net_impact,
+            "metadata": self.metadata,
+        }
+
+
+@dataclass
+class Anomaly:
+    """An anomaly detected in memory patterns."""
+
+    id: UUID
+    anomaly_type: AnomalyType
+    description: str
+    severity: float
+    episode_ids: list[UUID]
+    detected_at: datetime
+    baseline_value: float
+    observed_value: float
+    deviation_factor: float
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def is_critical(self) -> bool:
+        """Check if anomaly is critical (severity > 0.8)."""
+        return self.severity > 0.8
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "id": str(self.id),
+            "anomaly_type": self.anomaly_type.value,
+            "description": self.description,
+            "severity": self.severity,
+            "episode_ids": [str(eid) for eid in self.episode_ids],
+            "detected_at": self.detected_at.isoformat(),
+            "baseline_value": self.baseline_value,
+            "observed_value": self.observed_value,
+            "deviation_factor": self.deviation_factor,
+            "is_critical": self.is_critical,
+            "metadata": self.metadata,
+        }
+
+
+@dataclass
+class Insight:
+    """An actionable insight generated from reflection."""
+
+    id: UUID
+    insight_type: InsightType
+    title: str
+    description: str
+    priority: float
+    confidence: float
+    source_patterns: list[UUID]
+    source_factors: list[UUID]
+    source_anomalies: list[UUID]
+    recommended_actions: list[str]
+    generated_at: datetime
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    @property
+    def actionable_score(self) -> float:
+        """Calculate how actionable this insight is."""
+        action_weight = min(1.0, len(self.recommended_actions) / 3)
+        return self.priority * self.confidence * action_weight
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "id": str(self.id),
+            "insight_type": self.insight_type.value,
+            "title": self.title,
+            "description": self.description,
+            "priority": self.priority,
+            "confidence": self.confidence,
+            "source_patterns": [str(pid) for pid in self.source_patterns],
+            "source_factors": [str(fid) for fid in self.source_factors],
+            "source_anomalies": [str(aid) for aid in self.source_anomalies],
+            "recommended_actions": self.recommended_actions,
+            "generated_at": self.generated_at.isoformat(),
+            "actionable_score": self.actionable_score,
+            "metadata": self.metadata,
+        }
+
+
+@dataclass
+class ReflectionResult:
+    """Result of a reflection operation."""
+
+    patterns: list[Pattern]
+    factors: list[Factor]
+    anomalies: list[Anomaly]
+    insights: list[Insight]
+    time_range: TimeRange
+    episodes_analyzed: int
+    analysis_duration_seconds: float
+    generated_at: datetime = field(default_factory=_utcnow)
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "patterns": [p.to_dict() for p in self.patterns],
+            "factors": [f.to_dict() for f in self.factors],
+            "anomalies": [a.to_dict() for a in self.anomalies],
+            "insights": [i.to_dict() for i in self.insights],
+            "time_range": {
+                "start": self.time_range.start.isoformat(),
+                "end": self.time_range.end.isoformat(),
+                "duration_hours": self.time_range.duration_hours,
+            },
+            "episodes_analyzed": self.episodes_analyzed,
+            "analysis_duration_seconds": self.analysis_duration_seconds,
+            "generated_at": self.generated_at.isoformat(),
+        }
+
+    @property
+    def summary(self) -> str:
+        """Generate a summary of the reflection results."""
+        lines = [
+            f"Reflection Analysis ({self.time_range.duration_days:.1f} days)",
+            f"Episodes analyzed: {self.episodes_analyzed}",
+            "",
+            f"Patterns detected: {len(self.patterns)}",
+            f"Success/failure factors: {len(self.factors)}",
+            f"Anomalies found: {len(self.anomalies)}",
+            f"Insights generated: {len(self.insights)}",
+        ]
+
+        if self.insights:
+            lines.append("")
+            lines.append("Top insights:")
+            for insight in sorted(self.insights, key=lambda i: -i.priority)[:3]:
+                lines.append(f"  - [{insight.insight_type.value}] {insight.title}")
+
+        return "\n".join(lines)

backend/app/services/memory/scoping/__init__.py

@@ -0,0 +1,33 @@
+# app/services/memory/scoping/__init__.py
+"""
+Memory Scoping
+
+Hierarchical scoping for memory with inheritance:
+Global -> Project -> Agent Type -> Agent Instance -> Session
+"""
+
+from .resolver import (
+    ResolutionOptions,
+    ResolutionResult,
+    ScopeFilter,
+    ScopeResolver,
+    get_scope_resolver,
+)
+from .scope import (
+    ScopeInfo,
+    ScopeManager,
+    ScopePolicy,
+    get_scope_manager,
+)
+
+__all__ = [
+    "ResolutionOptions",
+    "ResolutionResult",
+    "ScopeFilter",
+    "ScopeInfo",
+    "ScopeManager",
+    "ScopePolicy",
+    "ScopeResolver",
+    "get_scope_manager",
+    "get_scope_resolver",
+]

backend/app/services/memory/scoping/resolver.py

@@ -0,0 +1,390 @@
+# app/services/memory/scoping/resolver.py
+"""
+Scope Resolution.
+
+Provides utilities for resolving memory queries across scope hierarchies,
+implementing inheritance and aggregation of memories from parent scopes.
+"""
+
+import logging
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Any, TypeVar
+
+from app.services.memory.types import ScopeContext, ScopeLevel
+
+from .scope import ScopeManager, get_scope_manager
+
+logger = logging.getLogger(__name__)
+
+T = TypeVar("T")
+
+
+@dataclass
+class ResolutionResult[T]:
+    """Result of a scope resolution."""
+
+    items: list[T]
+    sources: list[ScopeContext]
+    total_from_each: dict[str, int] = field(default_factory=dict)
+    inherited_count: int = 0
+    own_count: int = 0
+
+    @property
+    def total_count(self) -> int:
+        """Get total items from all sources."""
+        return len(self.items)
+
+
+@dataclass
+class ResolutionOptions:
+    """Options for scope resolution."""
+
+    include_inherited: bool = True
+    max_inheritance_depth: int = 5
+    limit_per_scope: int = 100
+    total_limit: int = 500
+    deduplicate: bool = True
+    deduplicate_key: str | None = None  # Field to use for deduplication
+
+
+class ScopeResolver:
+    """
+    Resolves memory queries across scope hierarchies.
+
+    Features:
+    - Traverse scope hierarchy for inherited memories
+    - Aggregate results from multiple scope levels
+    - Apply access control policies
+    - Support deduplication across scopes
+    """
+
+    def __init__(
+        self,
+        manager: ScopeManager | None = None,
+    ) -> None:
+        """
+        Initialize the resolver.
+
+        Args:
+            manager: Scope manager to use (defaults to singleton)
+        """
+        self._manager = manager or get_scope_manager()
+
+    def resolve(
+        self,
+        scope: ScopeContext,
+        fetcher: Callable[[ScopeContext, int], list[T]],
+        options: ResolutionOptions | None = None,
+    ) -> ResolutionResult[T]:
+        """
+        Resolve memories for a scope, including inherited memories.
+
+        Args:
+            scope: Starting scope
+            fetcher: Function to fetch items for a scope (scope, limit) -> items
+            options: Resolution options
+
+        Returns:
+            Resolution result with items from all scopes
+        """
+        opts = options or ResolutionOptions()
+
+        all_items: list[T] = []
+        sources: list[ScopeContext] = []
+        counts: dict[str, int] = {}
+        seen_keys: set[Any] = set()
+
+        # Collect scopes to query (starting from current, going up to ancestors)
+        scopes_to_query = self._collect_queryable_scopes(
+            scope=scope,
+            max_depth=opts.max_inheritance_depth if opts.include_inherited else 0,
+        )
+
+        own_count = 0
+        inherited_count = 0
+        remaining_limit = opts.total_limit
+
+        for i, query_scope in enumerate(scopes_to_query):
+            if remaining_limit <= 0:
+                break
+
+            # Check access policy
+            policy = self._manager.get_policy(query_scope)
+            if not policy.allows_read():
+                continue
+            if i > 0 and not policy.allows_inherit():
+                continue
+
+            # Fetch items for this scope
+            scope_limit = min(opts.limit_per_scope, remaining_limit)
+            items = fetcher(query_scope, scope_limit)
+
+            # Apply deduplication
+            if opts.deduplicate and opts.deduplicate_key:
+                items = self._deduplicate(items, opts.deduplicate_key, seen_keys)
+
+            if items:
+                all_items.extend(items)
+                sources.append(query_scope)
+                key = f"{query_scope.scope_type.value}:{query_scope.scope_id}"
+                counts[key] = len(items)
+
+                if i == 0:
+                    own_count = len(items)
+                else:
+                    inherited_count += len(items)
+
+                remaining_limit -= len(items)
+
+        logger.debug(
+            f"Resolved {len(all_items)} items from {len(sources)} scopes "
+            f"(own={own_count}, inherited={inherited_count})"
+        )
+
+        return ResolutionResult(
+            items=all_items[: opts.total_limit],
+            sources=sources,
+            total_from_each=counts,
+            own_count=own_count,
+            inherited_count=inherited_count,
+        )
+
+    def _collect_queryable_scopes(
+        self,
+        scope: ScopeContext,
+        max_depth: int,
+    ) -> list[ScopeContext]:
+        """Collect scopes to query, from current to ancestors."""
+        scopes: list[ScopeContext] = [scope]
+
+        if max_depth <= 0:
+            return scopes
+
+        current = scope.parent
+        depth = 0
+
+        while current is not None and depth < max_depth:
+            scopes.append(current)
+            current = current.parent
+            depth += 1
+
+        return scopes
+
+    def _deduplicate(
+        self,
+        items: list[T],
+        key_field: str,
+        seen_keys: set[Any],
+    ) -> list[T]:
+        """Remove duplicate items based on a key field."""
+        unique: list[T] = []
+
+        for item in items:
+            key = getattr(item, key_field, None)
+            if key is None:
+                # If no key, include the item
+                unique.append(item)
+            elif key not in seen_keys:
+                seen_keys.add(key)
+                unique.append(item)
+
+        return unique
+
+    def get_visible_scopes(
+        self,
+        scope: ScopeContext,
+    ) -> list[ScopeContext]:
+        """
+        Get all scopes visible from a given scope.
+
+        A scope can see itself and all its ancestors (if inheritance allowed).
+
+        Args:
+            scope: Starting scope
+
+        Returns:
+            List of visible scopes (from most specific to most general)
+        """
+        visible = [scope]
+
+        current = scope.parent
+        while current is not None:
+            policy = self._manager.get_policy(current)
+            if policy.allows_inherit():
+                visible.append(current)
+            else:
+                break  # Stop at first non-inheritable scope
+            current = current.parent
+
+        return visible
+
+    def find_write_scope(
+        self,
+        target_level: ScopeLevel,
+        scope: ScopeContext,
+    ) -> ScopeContext | None:
+        """
+        Find the appropriate scope for writing at a target level.
+
+        Walks up the hierarchy to find a scope at the target level
+        that allows writing.
+
+        Args:
+            target_level: Desired scope level
+            scope: Starting scope
+
+        Returns:
+            Scope to write to, or None if not found/not allowed
+        """
+        # First check if current scope is at target level
+        if scope.scope_type == target_level:
+            policy = self._manager.get_policy(scope)
+            return scope if policy.allows_write() else None
+
+        # Check ancestors
+        current = scope.parent
+        while current is not None:
+            if current.scope_type == target_level:
+                policy = self._manager.get_policy(current)
+                return current if policy.allows_write() else None
+            current = current.parent
+
+        return None
+
+    def resolve_scope_from_memory(
+        self,
+        memory_type: str,
+        project_id: str | None = None,
+        agent_type_id: str | None = None,
+        agent_instance_id: str | None = None,
+        session_id: str | None = None,
+    ) -> tuple[ScopeContext, ScopeLevel]:
+        """
+        Resolve the appropriate scope for a memory operation.
+
+        Different memory types have different scope requirements:
+        - working: Session or Agent Instance
+        - episodic: Agent Instance or Project
+        - semantic: Project or Global
+        - procedural: Agent Type or Project
+
+        Args:
+            memory_type: Type of memory
+            project_id: Project ID
+            agent_type_id: Agent type ID
+            agent_instance_id: Agent instance ID
+            session_id: Session ID
+
+        Returns:
+            Tuple of (scope context, recommended level)
+        """
+        # Build full scope chain
+        scope = self._manager.create_scope_from_ids(
+            project_id=project_id if project_id else None,  # type: ignore[arg-type]
+            agent_type_id=agent_type_id if agent_type_id else None,  # type: ignore[arg-type]
+            agent_instance_id=agent_instance_id if agent_instance_id else None,  # type: ignore[arg-type]
+            session_id=session_id,
+        )
+
+        # Determine recommended level based on memory type
+        recommended = self._get_recommended_level(memory_type)
+
+        return scope, recommended
+
+    def _get_recommended_level(self, memory_type: str) -> ScopeLevel:
+        """Get recommended scope level for a memory type."""
+        recommendations = {
+            "working": ScopeLevel.SESSION,
+            "episodic": ScopeLevel.AGENT_INSTANCE,
+            "semantic": ScopeLevel.PROJECT,
+            "procedural": ScopeLevel.AGENT_TYPE,
+        }
+        return recommendations.get(memory_type, ScopeLevel.PROJECT)
+
+    def validate_write_access(
+        self,
+        scope: ScopeContext,
+        memory_type: str,
+    ) -> bool:
+        """
+        Validate that writing is allowed for the given scope and memory type.
+
+        Args:
+            scope: Scope to validate
+            memory_type: Type of memory to write
+
+        Returns:
+            True if write is allowed
+        """
+        policy = self._manager.get_policy(scope)
+
+        if not policy.allows_write():
+            return False
+
+        if not policy.allows_memory_type(memory_type):
+            return False
+
+        return True
+
+    def get_scope_chain(
+        self,
+        scope: ScopeContext,
+    ) -> list[tuple[ScopeLevel, str]]:
+        """
+        Get the scope chain as a list of (level, id) tuples.
+
+        Args:
+            scope: Scope to get chain for
+
+        Returns:
+            List of (level, id) tuples from root to leaf
+        """
+        chain: list[tuple[ScopeLevel, str]] = []
+
+        # Get full hierarchy
+        hierarchy = scope.get_hierarchy()
+        for ctx in hierarchy:
+            chain.append((ctx.scope_type, ctx.scope_id))
+
+        return chain
+
+
+@dataclass
+class ScopeFilter:
+    """Filter for querying across scopes."""
+
+    scope_types: list[ScopeLevel] | None = None
+    project_ids: list[str] | None = None
+    agent_type_ids: list[str] | None = None
+    include_global: bool = True
+
+    def matches(self, scope: ScopeContext) -> bool:
+        """Check if a scope matches this filter."""
+        if self.scope_types and scope.scope_type not in self.scope_types:
+            return False
+
+        if scope.scope_type == ScopeLevel.GLOBAL:
+            return self.include_global
+
+        if scope.scope_type == ScopeLevel.PROJECT:
+            if self.project_ids and scope.scope_id not in self.project_ids:
+                return False
+
+        if scope.scope_type == ScopeLevel.AGENT_TYPE:
+            if self.agent_type_ids and scope.scope_id not in self.agent_type_ids:
+                return False
+
+        return True
+
+
+# Singleton resolver instance
+_resolver: ScopeResolver | None = None
+
+
+def get_scope_resolver() -> ScopeResolver:
+    """Get the singleton scope resolver instance."""
+    global _resolver
+    if _resolver is None:
+        _resolver = ScopeResolver()
+    return _resolver

backend/app/services/memory/scoping/scope.py

@@ -0,0 +1,472 @@
+# app/services/memory/scoping/scope.py
+"""
+Scope Management.
+
+Provides utilities for managing memory scopes with hierarchical inheritance:
+Global -> Project -> Agent Type -> Agent Instance -> Session
+"""
+
+import logging
+import threading
+from dataclasses import dataclass, field
+from typing import Any, ClassVar
+from uuid import UUID
+
+from app.services.memory.types import ScopeContext, ScopeLevel
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ScopePolicy:
+    """Access control policy for a scope."""
+
+    scope_type: ScopeLevel
+    scope_id: str
+    can_read: bool = True
+    can_write: bool = True
+    can_inherit: bool = True
+    allowed_memory_types: list[str] = field(default_factory=lambda: ["all"])
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    def allows_read(self) -> bool:
+        """Check if reading is allowed."""
+        return self.can_read
+
+    def allows_write(self) -> bool:
+        """Check if writing is allowed."""
+        return self.can_write
+
+    def allows_inherit(self) -> bool:
+        """Check if inheritance from parent is allowed."""
+        return self.can_inherit
+
+    def allows_memory_type(self, memory_type: str) -> bool:
+        """Check if a specific memory type is allowed."""
+        return (
+            "all" in self.allowed_memory_types
+            or memory_type in self.allowed_memory_types
+        )
+
+
+@dataclass
+class ScopeInfo:
+    """Information about a scope including its hierarchy."""
+
+    context: ScopeContext
+    policy: ScopePolicy
+    parent_info: "ScopeInfo | None" = None
+    child_count: int = 0
+    memory_count: int = 0
+
+    @property
+    def depth(self) -> int:
+        """Get the depth of this scope in the hierarchy."""
+        count = 0
+        current = self.parent_info
+        while current is not None:
+            count += 1
+            current = current.parent_info
+        return count
+
+
+class ScopeManager:
+    """
+    Manages memory scopes and their hierarchies.
+
+    Provides:
+    - Scope creation and validation
+    - Hierarchy management
+    - Access control policy management
+    - Scope inheritance rules
+    """
+
+    # Order of scope levels from root to leaf
+    SCOPE_ORDER: ClassVar[list[ScopeLevel]] = [
+        ScopeLevel.GLOBAL,
+        ScopeLevel.PROJECT,
+        ScopeLevel.AGENT_TYPE,
+        ScopeLevel.AGENT_INSTANCE,
+        ScopeLevel.SESSION,
+    ]
+
+    def __init__(self) -> None:
+        """Initialize the scope manager."""
+        # In-memory policy cache (would be backed by database in production)
+        self._policies: dict[str, ScopePolicy] = {}
+        self._default_policies = self._create_default_policies()
+
+    def _create_default_policies(self) -> dict[ScopeLevel, ScopePolicy]:
+        """Create default policies for each scope level."""
+        return {
+            ScopeLevel.GLOBAL: ScopePolicy(
+                scope_type=ScopeLevel.GLOBAL,
+                scope_id="global",
+                can_read=True,
+                can_write=False,  # Global writes require special permission
+                can_inherit=True,
+            ),
+            ScopeLevel.PROJECT: ScopePolicy(
+                scope_type=ScopeLevel.PROJECT,
+                scope_id="default",
+                can_read=True,
+                can_write=True,
+                can_inherit=True,
+            ),
+            ScopeLevel.AGENT_TYPE: ScopePolicy(
+                scope_type=ScopeLevel.AGENT_TYPE,
+                scope_id="default",
+                can_read=True,
+                can_write=True,
+                can_inherit=True,
+            ),
+            ScopeLevel.AGENT_INSTANCE: ScopePolicy(
+                scope_type=ScopeLevel.AGENT_INSTANCE,
+                scope_id="default",
+                can_read=True,
+                can_write=True,
+                can_inherit=True,
+            ),
+            ScopeLevel.SESSION: ScopePolicy(
+                scope_type=ScopeLevel.SESSION,
+                scope_id="default",
+                can_read=True,
+                can_write=True,
+                can_inherit=True,
+                allowed_memory_types=["working"],  # Sessions only allow working memory
+            ),
+        }
+
+    def create_scope(
+        self,
+        scope_type: ScopeLevel,
+        scope_id: str,
+        parent: ScopeContext | None = None,
+    ) -> ScopeContext:
+        """
+        Create a new scope context.
+
+        Args:
+            scope_type: Level of the scope
+            scope_id: Unique identifier within the level
+            parent: Optional parent scope
+
+        Returns:
+            Created scope context
+
+        Raises:
+            ValueError: If scope hierarchy is invalid
+        """
+        # Validate hierarchy
+        if parent is not None:
+            self._validate_parent_child(parent.scope_type, scope_type)
+
+        # For non-global scopes without parent, auto-create parent chain
+        if parent is None and scope_type != ScopeLevel.GLOBAL:
+            parent = self._create_parent_chain(scope_type, scope_id)
+
+        context = ScopeContext(
+            scope_type=scope_type,
+            scope_id=scope_id,
+            parent=parent,
+        )
+
+        logger.debug(f"Created scope: {scope_type.value}:{scope_id}")
+        return context
+
+    def _validate_parent_child(
+        self,
+        parent_type: ScopeLevel,
+        child_type: ScopeLevel,
+    ) -> None:
+        """Validate that parent-child relationship is valid."""
+        parent_idx = self.SCOPE_ORDER.index(parent_type)
+        child_idx = self.SCOPE_ORDER.index(child_type)
+
+        if child_idx <= parent_idx:
+            raise ValueError(
+                f"Invalid scope hierarchy: {child_type.value} cannot be child of {parent_type.value}"
+            )
+
+        # Allow skipping levels (e.g., PROJECT -> SESSION is valid)
+        # This enables flexible scope structures
+
+    def _create_parent_chain(
+        self,
+        target_type: ScopeLevel,
+        scope_id: str,
+    ) -> ScopeContext:
+        """Create parent scope chain up to target type."""
+        target_idx = self.SCOPE_ORDER.index(target_type)
+
+        # Start from global and build chain
+        current: ScopeContext | None = None
+
+        for i in range(target_idx):
+            level = self.SCOPE_ORDER[i]
+            if level == ScopeLevel.GLOBAL:
+                level_id = "global"
+            else:
+                # Use a default ID for intermediate levels
+                level_id = f"default_{level.value}"
+
+            current = ScopeContext(
+                scope_type=level,
+                scope_id=level_id,
+                parent=current,
+            )
+
+        return current  # type: ignore[return-value]
+
+    def create_scope_from_ids(
+        self,
+        project_id: UUID | None = None,
+        agent_type_id: UUID | None = None,
+        agent_instance_id: UUID | None = None,
+        session_id: str | None = None,
+    ) -> ScopeContext:
+        """
+        Create a scope context from individual IDs.
+
+        Automatically determines the most specific scope level
+        based on provided IDs.
+
+        Args:
+            project_id: Project UUID
+            agent_type_id: Agent type UUID
+            agent_instance_id: Agent instance UUID
+            session_id: Session identifier
+
+        Returns:
+            Scope context for the most specific level
+        """
+        # Build scope chain from most general to most specific
+        current: ScopeContext = ScopeContext(
+            scope_type=ScopeLevel.GLOBAL,
+            scope_id="global",
+            parent=None,
+        )
+
+        if project_id is not None:
+            current = ScopeContext(
+                scope_type=ScopeLevel.PROJECT,
+                scope_id=str(project_id),
+                parent=current,
+            )
+
+        if agent_type_id is not None:
+            current = ScopeContext(
+                scope_type=ScopeLevel.AGENT_TYPE,
+                scope_id=str(agent_type_id),
+                parent=current,
+            )
+
+        if agent_instance_id is not None:
+            current = ScopeContext(
+                scope_type=ScopeLevel.AGENT_INSTANCE,
+                scope_id=str(agent_instance_id),
+                parent=current,
+            )
+
+        if session_id is not None:
+            current = ScopeContext(
+                scope_type=ScopeLevel.SESSION,
+                scope_id=session_id,
+                parent=current,
+            )
+
+        return current
+
+    def get_policy(
+        self,
+        scope: ScopeContext,
+    ) -> ScopePolicy:
+        """
+        Get the access policy for a scope.
+
+        Args:
+            scope: Scope to get policy for
+
+        Returns:
+            Policy for the scope
+        """
+        key = self._scope_key(scope)
+
+        if key in self._policies:
+            return self._policies[key]
+
+        # Return default policy for the scope level
+        return self._default_policies.get(
+            scope.scope_type,
+            ScopePolicy(
+                scope_type=scope.scope_type,
+                scope_id=scope.scope_id,
+            ),
+        )
+
+    def set_policy(
+        self,
+        scope: ScopeContext,
+        policy: ScopePolicy,
+    ) -> None:
+        """
+        Set the access policy for a scope.
+
+        Args:
+            scope: Scope to set policy for
+            policy: Policy to apply
+        """
+        key = self._scope_key(scope)
+        self._policies[key] = policy
+        logger.info(f"Set policy for scope {key}")
+
+    def _scope_key(self, scope: ScopeContext) -> str:
+        """Generate a unique key for a scope."""
+        return f"{scope.scope_type.value}:{scope.scope_id}"
+
+    def get_scope_depth(self, scope_type: ScopeLevel) -> int:
+        """Get the depth of a scope level in the hierarchy."""
+        return self.SCOPE_ORDER.index(scope_type)
+
+    def get_parent_level(self, scope_type: ScopeLevel) -> ScopeLevel | None:
+        """Get the parent scope level for a given level."""
+        idx = self.SCOPE_ORDER.index(scope_type)
+        if idx == 0:
+            return None
+        return self.SCOPE_ORDER[idx - 1]
+
+    def get_child_level(self, scope_type: ScopeLevel) -> ScopeLevel | None:
+        """Get the child scope level for a given level."""
+        idx = self.SCOPE_ORDER.index(scope_type)
+        if idx >= len(self.SCOPE_ORDER) - 1:
+            return None
+        return self.SCOPE_ORDER[idx + 1]
+
+    def is_ancestor(
+        self,
+        potential_ancestor: ScopeContext,
+        descendant: ScopeContext,
+    ) -> bool:
+        """
+        Check if one scope is an ancestor of another.
+
+        Args:
+            potential_ancestor: Scope to check as ancestor
+            descendant: Scope to check as descendant
+
+        Returns:
+            True if ancestor relationship exists
+        """
+        current = descendant.parent
+        while current is not None:
+            if (
+                current.scope_type == potential_ancestor.scope_type
+                and current.scope_id == potential_ancestor.scope_id
+            ):
+                return True
+            current = current.parent
+        return False
+
+    def get_common_ancestor(
+        self,
+        scope_a: ScopeContext,
+        scope_b: ScopeContext,
+    ) -> ScopeContext | None:
+        """
+        Find the nearest common ancestor of two scopes.
+
+        Args:
+            scope_a: First scope
+            scope_b: Second scope
+
+        Returns:
+            Common ancestor or None if none exists
+        """
+        # Get ancestors of scope_a
+        ancestors_a: set[str] = set()
+        current: ScopeContext | None = scope_a
+        while current is not None:
+            ancestors_a.add(self._scope_key(current))
+            current = current.parent
+
+        # Find first ancestor of scope_b that's in ancestors_a
+        current = scope_b
+        while current is not None:
+            if self._scope_key(current) in ancestors_a:
+                return current
+            current = current.parent
+
+        return None
+
+    def can_access(
+        self,
+        accessor_scope: ScopeContext,
+        target_scope: ScopeContext,
+        operation: str = "read",
+    ) -> bool:
+        """
+        Check if accessor scope can access target scope.
+
+        Access rules:
+        - A scope can always access itself
+        - A scope can access ancestors (if inheritance allowed)
+        - A scope CANNOT access descendants (privacy)
+        - Sibling scopes cannot access each other
+
+        Args:
+            accessor_scope: Scope attempting access
+            target_scope: Scope being accessed
+            operation: Type of operation (read/write)
+
+        Returns:
+            True if access is allowed
+        """
+        # Same scope - always allowed
+        if (
+            accessor_scope.scope_type == target_scope.scope_type
+            and accessor_scope.scope_id == target_scope.scope_id
+        ):
+            policy = self.get_policy(target_scope)
+            if operation == "write":
+                return policy.allows_write()
+            return policy.allows_read()
+
+        # Check if target is ancestor (inheritance)
+        if self.is_ancestor(target_scope, accessor_scope):
+            policy = self.get_policy(target_scope)
+            if not policy.allows_inherit():
+                return False
+            if operation == "write":
+                return policy.allows_write()
+            return policy.allows_read()
+
+        # Check if accessor is ancestor of target (downward access)
+        # This is NOT allowed - parents cannot access children's memories
+        if self.is_ancestor(accessor_scope, target_scope):
+            return False
+
+        # Sibling scopes cannot access each other
+        return False
+
+
+# Singleton manager instance with thread-safe initialization
+_manager: ScopeManager | None = None
+_manager_lock = threading.Lock()
+
+
+def get_scope_manager() -> ScopeManager:
+    """Get the singleton scope manager instance (thread-safe)."""
+    global _manager
+    if _manager is None:
+        with _manager_lock:
+            # Double-check locking pattern
+            if _manager is None:
+                _manager = ScopeManager()
+    return _manager
+
+
+def reset_scope_manager() -> None:
+    """Reset the scope manager singleton (for testing)."""
+    global _manager
+    with _manager_lock:
+        _manager = None

backend/app/services/memory/semantic/__init__.py

@@ -0,0 +1,27 @@
+# app/services/memory/semantic/__init__.py
+"""
+Semantic Memory
+
+Fact storage with triple format (subject, predicate, object)
+and semantic search capabilities.
+"""
+
+from .extraction import (
+    ExtractedFact,
+    ExtractionContext,
+    FactExtractor,
+    get_fact_extractor,
+)
+from .memory import SemanticMemory
+from .verification import FactConflict, FactVerifier, VerificationResult
+
+__all__ = [
+    "ExtractedFact",
+    "ExtractionContext",
+    "FactConflict",
+    "FactExtractor",
+    "FactVerifier",
+    "SemanticMemory",
+    "VerificationResult",
+    "get_fact_extractor",
+]

backend/app/services/memory/semantic/extraction.py

@@ -0,0 +1,313 @@
+# app/services/memory/semantic/extraction.py
+"""
+Fact Extraction from Episodes.
+
+Provides utilities for extracting semantic facts (subject-predicate-object triples)
+from episodic memories and other text sources.
+"""
+
+import logging
+import re
+from dataclasses import dataclass, field
+from typing import Any, ClassVar
+
+from app.services.memory.types import Episode, FactCreate, Outcome
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ExtractionContext:
+    """Context for fact extraction."""
+
+    project_id: Any | None = None
+    source_episode_id: Any | None = None
+    min_confidence: float = 0.5
+    max_facts_per_source: int = 10
+
+
+@dataclass
+class ExtractedFact:
+    """A fact extracted from text before storage."""
+
+    subject: str
+    predicate: str
+    object: str
+    confidence: float
+    source_text: str = ""
+    metadata: dict[str, Any] = field(default_factory=dict)
+
+    def to_fact_create(
+        self,
+        project_id: Any | None = None,
+        source_episode_ids: list[Any] | None = None,
+    ) -> FactCreate:
+        """Convert to FactCreate for storage."""
+        return FactCreate(
+            subject=self.subject,
+            predicate=self.predicate,
+            object=self.object,
+            confidence=self.confidence,
+            project_id=project_id,
+            source_episode_ids=source_episode_ids or [],
+        )
+
+
+class FactExtractor:
+    """
+    Extracts facts from episodes and text.
+
+    This is a rule-based extractor. In production, this would be
+    replaced or augmented with LLM-based extraction for better accuracy.
+    """
+
+    # Common predicates we can detect
+    PREDICATE_PATTERNS: ClassVar[dict[str, str]] = {
+        "uses": r"(?:uses?|using|utilizes?)",
+        "requires": r"(?:requires?|needs?|depends?\s+on)",
+        "is_a": r"(?:is\s+a|is\s+an|are\s+a|are)",
+        "has": r"(?:has|have|contains?)",
+        "part_of": r"(?:part\s+of|belongs?\s+to|member\s+of)",
+        "causes": r"(?:causes?|leads?\s+to|results?\s+in)",
+        "prevents": r"(?:prevents?|avoids?|stops?)",
+        "solves": r"(?:solves?|fixes?|resolves?)",
+    }
+
+    def __init__(self) -> None:
+        """Initialize extractor."""
+        self._compiled_patterns = {
+            pred: re.compile(pattern, re.IGNORECASE)
+            for pred, pattern in self.PREDICATE_PATTERNS.items()
+        }
+
+    def extract_from_episode(
+        self,
+        episode: Episode,
+        context: ExtractionContext | None = None,
+    ) -> list[ExtractedFact]:
+        """
+        Extract facts from an episode.
+
+        Args:
+            episode: Episode to extract from
+            context: Optional extraction context
+
+        Returns:
+            List of extracted facts
+        """
+        ctx = context or ExtractionContext()
+        facts: list[ExtractedFact] = []
+
+        # Extract from task description
+        task_facts = self._extract_from_text(
+            episode.task_description,
+            source_prefix=episode.task_type,
+        )
+        facts.extend(task_facts)
+
+        # Extract from lessons learned
+        for lesson in episode.lessons_learned:
+            lesson_facts = self._extract_from_lesson(lesson, episode)
+            facts.extend(lesson_facts)
+
+        # Extract outcome-based facts
+        outcome_facts = self._extract_outcome_facts(episode)
+        facts.extend(outcome_facts)
+
+        # Limit and filter
+        facts = [f for f in facts if f.confidence >= ctx.min_confidence]
+        facts = facts[: ctx.max_facts_per_source]
+
+        logger.debug(f"Extracted {len(facts)} facts from episode {episode.id}")
+
+        return facts
+
+    def _extract_from_text(
+        self,
+        text: str,
+        source_prefix: str = "",
+    ) -> list[ExtractedFact]:
+        """Extract facts from free-form text using pattern matching."""
+        facts: list[ExtractedFact] = []
+
+        if not text or len(text) < 10:
+            return facts
+
+        # Split into sentences
+        sentences = re.split(r"[.!?]+", text)
+
+        for sentence in sentences:
+            sentence = sentence.strip()
+            if len(sentence) < 10:
+                continue
+
+            # Try to match predicate patterns
+            for predicate, pattern in self._compiled_patterns.items():
+                match = pattern.search(sentence)
+                if match:
+                    # Extract subject (text before predicate)
+                    subject = sentence[: match.start()].strip()
+                    # Extract object (text after predicate)
+                    obj = sentence[match.end() :].strip()
+
+                    if len(subject) > 2 and len(obj) > 2:
+                        facts.append(
+                            ExtractedFact(
+                                subject=subject[:200],  # Limit length
+                                predicate=predicate,
+                                object=obj[:500],
+                                confidence=0.6,  # Medium confidence for pattern matching
+                                source_text=sentence,
+                            )
+                        )
+                        break  # One fact per sentence
+
+        return facts
+
+    def _extract_from_lesson(
+        self,
+        lesson: str,
+        episode: Episode,
+    ) -> list[ExtractedFact]:
+        """Extract facts from a lesson learned."""
+        facts: list[ExtractedFact] = []
+
+        if not lesson or len(lesson) < 10:
+            return facts
+
+        # Lessons are typically in the form "Always do X" or "Never do Y"
+        # or "When X, do Y"
+
+        # Direct lesson fact
+        facts.append(
+            ExtractedFact(
+                subject=episode.task_type,
+                predicate="lesson_learned",
+                object=lesson,
+                confidence=0.8,  # High confidence for explicit lessons
+                source_text=lesson,
+                metadata={"outcome": episode.outcome.value},
+            )
+        )
+
+        # Extract conditional patterns
+        conditional_match = re.match(
+            r"(?:when|if)\s+(.+?),\s*(.+)",
+            lesson,
+            re.IGNORECASE,
+        )
+        if conditional_match:
+            condition, action = conditional_match.groups()
+            facts.append(
+                ExtractedFact(
+                    subject=condition.strip(),
+                    predicate="requires_action",
+                    object=action.strip(),
+                    confidence=0.7,
+                    source_text=lesson,
+                )
+            )
+
+        # Extract "always/never" patterns
+        always_match = re.match(
+            r"(?:always)\s+(.+)",
+            lesson,
+            re.IGNORECASE,
+        )
+        if always_match:
+            facts.append(
+                ExtractedFact(
+                    subject=episode.task_type,
+                    predicate="best_practice",
+                    object=always_match.group(1).strip(),
+                    confidence=0.85,
+                    source_text=lesson,
+                )
+            )
+
+        never_match = re.match(
+            r"(?:never|avoid)\s+(.+)",
+            lesson,
+            re.IGNORECASE,
+        )
+        if never_match:
+            facts.append(
+                ExtractedFact(
+                    subject=episode.task_type,
+                    predicate="anti_pattern",
+                    object=never_match.group(1).strip(),
+                    confidence=0.85,
+                    source_text=lesson,
+                )
+            )
+
+        return facts
+
+    def _extract_outcome_facts(
+        self,
+        episode: Episode,
+    ) -> list[ExtractedFact]:
+        """Extract facts based on episode outcome."""
+        facts: list[ExtractedFact] = []
+
+        # Create fact based on outcome
+        if episode.outcome == Outcome.SUCCESS:
+            if episode.outcome_details:
+                facts.append(
+                    ExtractedFact(
+                        subject=episode.task_type,
+                        predicate="successful_approach",
+                        object=episode.outcome_details[:500],
+                        confidence=0.75,
+                        source_text=episode.outcome_details,
+                    )
+                )
+        elif episode.outcome == Outcome.FAILURE:
+            if episode.outcome_details:
+                facts.append(
+                    ExtractedFact(
+                        subject=episode.task_type,
+                        predicate="known_failure_mode",
+                        object=episode.outcome_details[:500],
+                        confidence=0.8,  # High confidence for failures
+                        source_text=episode.outcome_details,
+                    )
+                )
+
+        return facts
+
+    def extract_from_text(
+        self,
+        text: str,
+        context: ExtractionContext | None = None,
+    ) -> list[ExtractedFact]:
+        """
+        Extract facts from arbitrary text.
+
+        Args:
+            text: Text to extract from
+            context: Optional extraction context
+
+        Returns:
+            List of extracted facts
+        """
+        ctx = context or ExtractionContext()
+
+        facts = self._extract_from_text(text)
+
+        # Filter by confidence
+        facts = [f for f in facts if f.confidence >= ctx.min_confidence]
+
+        return facts[: ctx.max_facts_per_source]
+
+
+# Singleton extractor instance
+_extractor: FactExtractor | None = None
+
+
+def get_fact_extractor() -> FactExtractor:
+    """Get the singleton fact extractor instance."""
+    global _extractor
+    if _extractor is None:
+        _extractor = FactExtractor()
+    return _extractor

backend/app/services/memory/semantic/memory.py

@@ -0,0 +1,767 @@
+# app/services/memory/semantic/memory.py
+"""
+Semantic Memory Implementation.
+
+Provides fact storage and retrieval using subject-predicate-object triples.
+Supports semantic search, confidence scoring, and fact reinforcement.
+"""
+
+import logging
+import time
+from datetime import UTC, datetime
+from typing import Any
+from uuid import UUID
+
+from sqlalchemy import and_, desc, or_, select, update
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.memory.fact import Fact as FactModel
+from app.services.memory.config import get_memory_settings
+from app.services.memory.types import Episode, Fact, FactCreate, RetrievalResult
+
+logger = logging.getLogger(__name__)
+
+
+def _escape_like_pattern(pattern: str) -> str:
+    """
+    Escape SQL LIKE/ILIKE special characters to prevent pattern injection.
+
+    Characters escaped:
+    - % (matches zero or more characters)
+    - _ (matches exactly one character)
+    - \\ (escape character itself)
+
+    Args:
+        pattern: Raw search pattern from user input
+
+    Returns:
+        Escaped pattern safe for use in LIKE/ILIKE queries
+    """
+    # Escape backslash first, then the wildcards
+    return pattern.replace("\\", "\\\\").replace("%", "\\%").replace("_", "\\_")
+
+
+def _model_to_fact(model: FactModel) -> Fact:
+    """Convert SQLAlchemy model to Fact dataclass."""
+    # SQLAlchemy Column types are inferred as Column[T] by mypy, but at runtime
+    # they return actual values. We use type: ignore to handle this mismatch.
+    return Fact(
+        id=model.id,  # type: ignore[arg-type]
+        project_id=model.project_id,  # type: ignore[arg-type]
+        subject=model.subject,  # type: ignore[arg-type]
+        predicate=model.predicate,  # type: ignore[arg-type]
+        object=model.object,  # type: ignore[arg-type]
+        confidence=model.confidence,  # type: ignore[arg-type]
+        source_episode_ids=model.source_episode_ids or [],  # type: ignore[arg-type]
+        first_learned=model.first_learned,  # type: ignore[arg-type]
+        last_reinforced=model.last_reinforced,  # type: ignore[arg-type]
+        reinforcement_count=model.reinforcement_count,  # type: ignore[arg-type]
+        embedding=None,  # Don't expose raw embedding
+        created_at=model.created_at,  # type: ignore[arg-type]
+        updated_at=model.updated_at,  # type: ignore[arg-type]
+    )
+
+
+class SemanticMemory:
+    """
+    Semantic Memory Service.
+
+    Provides fact storage and retrieval:
+    - Store facts as subject-predicate-object triples
+    - Semantic search over facts
+    - Entity-based retrieval
+    - Confidence scoring and decay
+    - Fact reinforcement on repeated learning
+    - Conflict resolution
+
+    Performance target: <100ms P95 for retrieval
+    """
+
+    def __init__(
+        self,
+        session: AsyncSession,
+        embedding_generator: Any | None = None,
+    ) -> None:
+        """
+        Initialize semantic memory.
+
+        Args:
+            session: Database session
+            embedding_generator: Optional embedding generator for semantic search
+        """
+        self._session = session
+        self._embedding_generator = embedding_generator
+        self._settings = get_memory_settings()
+
+    @classmethod
+    async def create(
+        cls,
+        session: AsyncSession,
+        embedding_generator: Any | None = None,
+    ) -> "SemanticMemory":
+        """
+        Factory method to create SemanticMemory.
+
+        Args:
+            session: Database session
+            embedding_generator: Optional embedding generator
+
+        Returns:
+            Configured SemanticMemory instance
+        """
+        return cls(session=session, embedding_generator=embedding_generator)
+
+    # =========================================================================
+    # Fact Storage
+    # =========================================================================
+
+    async def store_fact(self, fact: FactCreate) -> Fact:
+        """
+        Store a new fact or reinforce an existing one.
+
+        If a fact with the same triple (subject, predicate, object) exists
+        in the same scope, it will be reinforced instead of duplicated.
+
+        Args:
+            fact: Fact data to store
+
+        Returns:
+            The created or reinforced fact
+        """
+        # Check for existing fact with same triple
+        existing = await self._find_existing_fact(
+            project_id=fact.project_id,
+            subject=fact.subject,
+            predicate=fact.predicate,
+            object=fact.object,
+        )
+
+        if existing is not None:
+            # Reinforce existing fact
+            return await self.reinforce_fact(
+                existing.id,  # type: ignore[arg-type]
+                source_episode_ids=fact.source_episode_ids,
+            )
+
+        # Create new fact
+        now = datetime.now(UTC)
+
+        # Generate embedding if possible
+        embedding = None
+        if self._embedding_generator is not None:
+            embedding_text = self._create_embedding_text(fact)
+            embedding = await self._embedding_generator.generate(embedding_text)
+
+        model = FactModel(
+            project_id=fact.project_id,
+            subject=fact.subject,
+            predicate=fact.predicate,
+            object=fact.object,
+            confidence=fact.confidence,
+            source_episode_ids=fact.source_episode_ids,
+            first_learned=now,
+            last_reinforced=now,
+            reinforcement_count=1,
+            embedding=embedding,
+        )
+
+        self._session.add(model)
+        await self._session.flush()
+        await self._session.refresh(model)
+
+        logger.info(
+            f"Stored new fact: {fact.subject} - {fact.predicate} - {fact.object[:50]}..."
+        )
+
+        return _model_to_fact(model)
+
+    async def _find_existing_fact(
+        self,
+        project_id: UUID | None,
+        subject: str,
+        predicate: str,
+        object: str,
+    ) -> FactModel | None:
+        """Find an existing fact with the same triple in the same scope."""
+        query = select(FactModel).where(
+            and_(
+                FactModel.subject == subject,
+                FactModel.predicate == predicate,
+                FactModel.object == object,
+            )
+        )
+
+        if project_id is not None:
+            query = query.where(FactModel.project_id == project_id)
+        else:
+            query = query.where(FactModel.project_id.is_(None))
+
+        result = await self._session.execute(query)
+        return result.scalar_one_or_none()
+
+    def _create_embedding_text(self, fact: FactCreate) -> str:
+        """Create text for embedding from fact data."""
+        return f"{fact.subject} {fact.predicate} {fact.object}"
+
+    # =========================================================================
+    # Fact Retrieval
+    # =========================================================================
+
+    async def search_facts(
+        self,
+        query: str,
+        project_id: UUID | None = None,
+        limit: int = 10,
+        min_confidence: float | None = None,
+    ) -> list[Fact]:
+        """
+        Search for facts semantically similar to the query.
+
+        Args:
+            query: Search query
+            project_id: Optional project to search within
+            limit: Maximum results
+            min_confidence: Optional minimum confidence filter
+
+        Returns:
+            List of matching facts
+        """
+        result = await self._search_facts_with_metadata(
+            query=query,
+            project_id=project_id,
+            limit=limit,
+            min_confidence=min_confidence,
+        )
+        return result.items
+
+    async def _search_facts_with_metadata(
+        self,
+        query: str,
+        project_id: UUID | None = None,
+        limit: int = 10,
+        min_confidence: float | None = None,
+    ) -> RetrievalResult[Fact]:
+        """Search facts with full result metadata."""
+        start_time = time.perf_counter()
+
+        min_conf = min_confidence or self._settings.semantic_min_confidence
+
+        # Build base query
+        stmt = (
+            select(FactModel)
+            .where(FactModel.confidence >= min_conf)
+            .order_by(desc(FactModel.confidence), desc(FactModel.last_reinforced))
+            .limit(limit)
+        )
+
+        # Apply project filter
+        if project_id is not None:
+            # Include both project-specific and global facts
+            stmt = stmt.where(
+                or_(
+                    FactModel.project_id == project_id,
+                    FactModel.project_id.is_(None),
+                )
+            )
+
+        # TODO: Implement proper vector similarity search when pgvector is integrated
+        # For now, do text-based search on subject/predicate/object
+        search_terms = query.lower().split()
+        if search_terms:
+            conditions = []
+            for term in search_terms[:5]:  # Limit to 5 terms
+                # Escape SQL wildcards to prevent pattern injection
+                escaped_term = _escape_like_pattern(term)
+                term_pattern = f"%{escaped_term}%"
+                conditions.append(
+                    or_(
+                        FactModel.subject.ilike(term_pattern),
+                        FactModel.predicate.ilike(term_pattern),
+                        FactModel.object.ilike(term_pattern),
+                    )
+                )
+            if conditions:
+                stmt = stmt.where(or_(*conditions))
+
+        result = await self._session.execute(stmt)
+        models = list(result.scalars().all())
+
+        latency_ms = (time.perf_counter() - start_time) * 1000
+
+        return RetrievalResult(
+            items=[_model_to_fact(m) for m in models],
+            total_count=len(models),
+            query=query,
+            retrieval_type="semantic",
+            latency_ms=latency_ms,
+            metadata={"min_confidence": min_conf},
+        )
+
+    async def get_by_entity(
+        self,
+        entity: str,
+        project_id: UUID | None = None,
+        limit: int = 20,
+    ) -> list[Fact]:
+        """
+        Get facts related to an entity (as subject or object).
+
+        Args:
+            entity: Entity to search for
+            project_id: Optional project to search within
+            limit: Maximum results
+
+        Returns:
+            List of facts mentioning the entity
+        """
+        start_time = time.perf_counter()
+
+        # Escape SQL wildcards to prevent pattern injection
+        escaped_entity = _escape_like_pattern(entity)
+        entity_pattern = f"%{escaped_entity}%"
+
+        stmt = (
+            select(FactModel)
+            .where(
+                or_(
+                    FactModel.subject.ilike(entity_pattern),
+                    FactModel.object.ilike(entity_pattern),
+                )
+            )
+            .order_by(desc(FactModel.confidence), desc(FactModel.last_reinforced))
+            .limit(limit)
+        )
+
+        if project_id is not None:
+            stmt = stmt.where(
+                or_(
+                    FactModel.project_id == project_id,
+                    FactModel.project_id.is_(None),
+                )
+            )
+
+        result = await self._session.execute(stmt)
+        models = list(result.scalars().all())
+
+        latency_ms = (time.perf_counter() - start_time) * 1000
+        logger.debug(
+            f"get_by_entity({entity}) returned {len(models)} facts in {latency_ms:.1f}ms"
+        )
+
+        return [_model_to_fact(m) for m in models]
+
+    async def get_by_subject(
+        self,
+        subject: str,
+        project_id: UUID | None = None,
+        predicate: str | None = None,
+        limit: int = 20,
+    ) -> list[Fact]:
+        """
+        Get facts with a specific subject.
+
+        Args:
+            subject: Subject to search for
+            project_id: Optional project to search within
+            predicate: Optional predicate filter
+            limit: Maximum results
+
+        Returns:
+            List of facts with matching subject
+        """
+        stmt = (
+            select(FactModel)
+            .where(FactModel.subject == subject)
+            .order_by(desc(FactModel.confidence))
+            .limit(limit)
+        )
+
+        if predicate is not None:
+            stmt = stmt.where(FactModel.predicate == predicate)
+
+        if project_id is not None:
+            stmt = stmt.where(
+                or_(
+                    FactModel.project_id == project_id,
+                    FactModel.project_id.is_(None),
+                )
+            )
+
+        result = await self._session.execute(stmt)
+        models = list(result.scalars().all())
+
+        return [_model_to_fact(m) for m in models]
+
+    async def get_by_id(self, fact_id: UUID) -> Fact | None:
+        """Get a fact by ID."""
+        query = select(FactModel).where(FactModel.id == fact_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+        return _model_to_fact(model) if model else None
+
+    # =========================================================================
+    # Fact Reinforcement
+    # =========================================================================
+
+    async def reinforce_fact(
+        self,
+        fact_id: UUID,
+        confidence_boost: float = 0.1,
+        source_episode_ids: list[UUID] | None = None,
+    ) -> Fact:
+        """
+        Reinforce a fact, increasing its confidence.
+
+        Args:
+            fact_id: Fact to reinforce
+            confidence_boost: Amount to increase confidence (default 0.1)
+            source_episode_ids: Additional source episodes
+
+        Returns:
+            Updated fact
+
+        Raises:
+            ValueError: If fact not found
+        """
+        query = select(FactModel).where(FactModel.id == fact_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+
+        if model is None:
+            raise ValueError(f"Fact not found: {fact_id}")
+
+        # Calculate new confidence (max 1.0)
+        current_confidence: float = model.confidence  # type: ignore[assignment]
+        new_confidence = min(1.0, current_confidence + confidence_boost)
+
+        # Merge source episode IDs
+        current_sources: list[UUID] = model.source_episode_ids or []  # type: ignore[assignment]
+        if source_episode_ids:
+            # Add new sources, avoiding duplicates
+            new_sources = list(set(current_sources + source_episode_ids))
+        else:
+            new_sources = current_sources
+
+        now = datetime.now(UTC)
+        stmt = (
+            update(FactModel)
+            .where(FactModel.id == fact_id)
+            .values(
+                confidence=new_confidence,
+                source_episode_ids=new_sources,
+                last_reinforced=now,
+                reinforcement_count=FactModel.reinforcement_count + 1,
+                updated_at=now,
+            )
+            .returning(FactModel)
+        )
+
+        result = await self._session.execute(stmt)
+        updated_model = result.scalar_one()
+        await self._session.flush()
+
+        logger.info(
+            f"Reinforced fact {fact_id}: confidence {current_confidence:.2f} -> {new_confidence:.2f}"
+        )
+
+        return _model_to_fact(updated_model)
+
+    async def deprecate_fact(
+        self,
+        fact_id: UUID,
+        reason: str,
+        new_confidence: float = 0.0,
+    ) -> Fact | None:
+        """
+        Deprecate a fact by lowering its confidence.
+
+        Args:
+            fact_id: Fact to deprecate
+            reason: Reason for deprecation
+            new_confidence: New confidence level (default 0.0)
+
+        Returns:
+            Updated fact or None if not found
+        """
+        query = select(FactModel).where(FactModel.id == fact_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+
+        if model is None:
+            return None
+
+        now = datetime.now(UTC)
+        stmt = (
+            update(FactModel)
+            .where(FactModel.id == fact_id)
+            .values(
+                confidence=max(0.0, new_confidence),
+                updated_at=now,
+            )
+            .returning(FactModel)
+        )
+
+        result = await self._session.execute(stmt)
+        updated_model = result.scalar_one_or_none()
+        await self._session.flush()
+
+        logger.info(f"Deprecated fact {fact_id}: {reason}")
+
+        return _model_to_fact(updated_model) if updated_model else None
+
+    # =========================================================================
+    # Fact Extraction from Episodes
+    # =========================================================================
+
+    async def extract_facts_from_episode(
+        self,
+        episode: Episode,
+    ) -> list[Fact]:
+        """
+        Extract facts from an episode.
+
+        This is a placeholder for LLM-based fact extraction.
+        In production, this would call an LLM to analyze the episode
+        and extract subject-predicate-object triples.
+
+        Args:
+            episode: Episode to extract facts from
+
+        Returns:
+            List of extracted facts
+        """
+        # For now, extract basic facts from lessons learned
+        extracted_facts: list[Fact] = []
+
+        for lesson in episode.lessons_learned:
+            if len(lesson) > 10:  # Skip very short lessons
+                fact_create = FactCreate(
+                    subject=episode.task_type,
+                    predicate="lesson_learned",
+                    object=lesson,
+                    confidence=0.7,  # Lessons start with moderate confidence
+                    project_id=episode.project_id,
+                    source_episode_ids=[episode.id],
+                )
+                fact = await self.store_fact(fact_create)
+                extracted_facts.append(fact)
+
+        logger.debug(
+            f"Extracted {len(extracted_facts)} facts from episode {episode.id}"
+        )
+
+        return extracted_facts
+
+    # =========================================================================
+    # Conflict Resolution
+    # =========================================================================
+
+    async def resolve_conflict(
+        self,
+        fact_ids: list[UUID],
+        keep_fact_id: UUID | None = None,
+    ) -> Fact | None:
+        """
+        Resolve a conflict between multiple facts.
+
+        If keep_fact_id is specified, that fact is kept and others are deprecated.
+        Otherwise, the fact with highest confidence is kept.
+
+        Args:
+            fact_ids: IDs of conflicting facts
+            keep_fact_id: Optional ID of fact to keep
+
+        Returns:
+            The winning fact, or None if no facts found
+        """
+        if not fact_ids:
+            return None
+
+        # Load all facts
+        query = select(FactModel).where(FactModel.id.in_(fact_ids))
+        result = await self._session.execute(query)
+        models = list(result.scalars().all())
+
+        if not models:
+            return None
+
+        # Determine winner
+        if keep_fact_id is not None:
+            winner = next((m for m in models if m.id == keep_fact_id), None)
+            if winner is None:
+                # Fallback to highest confidence
+                winner = max(models, key=lambda m: m.confidence)
+        else:
+            # Keep the fact with highest confidence
+            winner = max(models, key=lambda m: m.confidence)
+
+        # Deprecate losers
+        for model in models:
+            if model.id != winner.id:
+                await self.deprecate_fact(
+                    model.id,  # type: ignore[arg-type]
+                    reason=f"Conflict resolution: superseded by {winner.id}",
+                )
+
+        logger.info(
+            f"Resolved conflict between {len(fact_ids)} facts, keeping {winner.id}"
+        )
+
+        return _model_to_fact(winner)
+
+    # =========================================================================
+    # Confidence Decay
+    # =========================================================================
+
+    async def apply_confidence_decay(
+        self,
+        project_id: UUID | None = None,
+        decay_factor: float = 0.01,
+    ) -> int:
+        """
+        Apply confidence decay to facts that haven't been reinforced recently.
+
+        Args:
+            project_id: Optional project to apply decay to
+            decay_factor: Decay factor per day (default 0.01)
+
+        Returns:
+            Number of facts affected
+        """
+        now = datetime.now(UTC)
+        decay_days = self._settings.semantic_confidence_decay_days
+        min_conf = self._settings.semantic_min_confidence
+
+        # Calculate cutoff date
+        from datetime import timedelta
+
+        cutoff = now - timedelta(days=decay_days)
+
+        # Find facts needing decay
+        query = select(FactModel).where(
+            and_(
+                FactModel.last_reinforced < cutoff,
+                FactModel.confidence > min_conf,
+            )
+        )
+
+        if project_id is not None:
+            query = query.where(FactModel.project_id == project_id)
+
+        result = await self._session.execute(query)
+        models = list(result.scalars().all())
+
+        # Apply decay
+        updated_count = 0
+        for model in models:
+            # Calculate days since last reinforcement
+            days_since: float = (now - model.last_reinforced).days
+
+            # Calculate decay: exponential decay based on days
+            decay = decay_factor * (days_since - decay_days)
+            new_confidence = max(min_conf, model.confidence - decay)
+
+            if new_confidence != model.confidence:
+                await self._session.execute(
+                    update(FactModel)
+                    .where(FactModel.id == model.id)
+                    .values(confidence=new_confidence, updated_at=now)
+                )
+                updated_count += 1
+
+        await self._session.flush()
+        logger.info(f"Applied confidence decay to {updated_count} facts")
+
+        return updated_count
+
+    # =========================================================================
+    # Statistics
+    # =========================================================================
+
+    async def get_stats(self, project_id: UUID | None = None) -> dict[str, Any]:
+        """
+        Get statistics about semantic memory.
+
+        Args:
+            project_id: Optional project to get stats for
+
+        Returns:
+            Dictionary with statistics
+        """
+        # Get all facts for this scope
+        query = select(FactModel)
+        if project_id is not None:
+            query = query.where(
+                or_(
+                    FactModel.project_id == project_id,
+                    FactModel.project_id.is_(None),
+                )
+            )
+
+        result = await self._session.execute(query)
+        models = list(result.scalars().all())
+
+        if not models:
+            return {
+                "total_facts": 0,
+                "avg_confidence": 0.0,
+                "avg_reinforcement_count": 0.0,
+                "high_confidence_count": 0,
+                "low_confidence_count": 0,
+            }
+
+        confidences = [m.confidence for m in models]
+        reinforcements = [m.reinforcement_count for m in models]
+
+        return {
+            "total_facts": len(models),
+            "avg_confidence": sum(confidences) / len(confidences),
+            "avg_reinforcement_count": sum(reinforcements) / len(reinforcements),
+            "high_confidence_count": sum(1 for c in confidences if c >= 0.8),
+            "low_confidence_count": sum(1 for c in confidences if c < 0.5),
+        }
+
+    async def count(self, project_id: UUID | None = None) -> int:
+        """
+        Count facts in scope.
+
+        Args:
+            project_id: Optional project to count for
+
+        Returns:
+            Number of facts
+        """
+        query = select(FactModel)
+        if project_id is not None:
+            query = query.where(
+                or_(
+                    FactModel.project_id == project_id,
+                    FactModel.project_id.is_(None),
+                )
+            )
+
+        result = await self._session.execute(query)
+        return len(list(result.scalars().all()))
+
+    async def delete(self, fact_id: UUID) -> bool:
+        """
+        Delete a fact.
+
+        Args:
+            fact_id: Fact to delete
+
+        Returns:
+            True if deleted, False if not found
+        """
+        query = select(FactModel).where(FactModel.id == fact_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+
+        if model is None:
+            return False
+
+        await self._session.delete(model)
+        await self._session.flush()
+
+        logger.info(f"Deleted fact {fact_id}")
+        return True

backend/app/services/memory/semantic/verification.py

@@ -0,0 +1,363 @@
+# app/services/memory/semantic/verification.py
+"""
+Fact Verification.
+
+Provides utilities for verifying facts, detecting conflicts,
+and managing fact consistency.
+"""
+
+import logging
+from dataclasses import dataclass, field
+from typing import Any, ClassVar
+from uuid import UUID
+
+from sqlalchemy import and_, or_, select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.models.memory.fact import Fact as FactModel
+from app.services.memory.types import Fact
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class VerificationResult:
+    """Result of fact verification."""
+
+    is_valid: bool
+    confidence_adjustment: float = 0.0
+    conflicts: list["FactConflict"] = field(default_factory=list)
+    supporting_facts: list[Fact] = field(default_factory=list)
+    messages: list[str] = field(default_factory=list)
+
+
+@dataclass
+class FactConflict:
+    """Represents a conflict between two facts."""
+
+    fact_a_id: UUID
+    fact_b_id: UUID
+    conflict_type: str  # "contradiction", "superseded", "partial_overlap"
+    description: str
+    suggested_resolution: str | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        """Convert to dictionary."""
+        return {
+            "fact_a_id": str(self.fact_a_id),
+            "fact_b_id": str(self.fact_b_id),
+            "conflict_type": self.conflict_type,
+            "description": self.description,
+            "suggested_resolution": self.suggested_resolution,
+        }
+
+
+class FactVerifier:
+    """
+    Verifies facts and detects conflicts.
+
+    Provides methods to:
+    - Check if a fact conflicts with existing facts
+    - Find supporting evidence for a fact
+    - Detect contradictions in the fact base
+    """
+
+    # Predicates that are opposites/contradictions
+    CONTRADICTORY_PREDICATES: ClassVar[set[tuple[str, str]]] = {
+        ("uses", "does_not_use"),
+        ("requires", "does_not_require"),
+        ("is_a", "is_not_a"),
+        ("causes", "prevents"),
+        ("allows", "prevents"),
+        ("supports", "does_not_support"),
+        ("best_practice", "anti_pattern"),
+    }
+
+    def __init__(self, session: AsyncSession) -> None:
+        """Initialize verifier with database session."""
+        self._session = session
+
+    async def verify_fact(
+        self,
+        subject: str,
+        predicate: str,
+        obj: str,
+        project_id: UUID | None = None,
+    ) -> VerificationResult:
+        """
+        Verify a fact against existing facts.
+
+        Args:
+            subject: Fact subject
+            predicate: Fact predicate
+            obj: Fact object
+            project_id: Optional project scope
+
+        Returns:
+            VerificationResult with verification details
+        """
+        result = VerificationResult(is_valid=True)
+
+        # Check for direct contradictions
+        conflicts = await self._find_contradictions(
+            subject=subject,
+            predicate=predicate,
+            obj=obj,
+            project_id=project_id,
+        )
+        result.conflicts = conflicts
+
+        if conflicts:
+            result.is_valid = False
+            result.messages.append(f"Found {len(conflicts)} conflicting fact(s)")
+            # Reduce confidence based on conflicts
+            result.confidence_adjustment = -0.1 * len(conflicts)
+
+        # Find supporting facts
+        supporting = await self._find_supporting_facts(
+            subject=subject,
+            predicate=predicate,
+            project_id=project_id,
+        )
+        result.supporting_facts = supporting
+
+        if supporting:
+            result.messages.append(f"Found {len(supporting)} supporting fact(s)")
+            # Boost confidence based on support
+            result.confidence_adjustment += 0.05 * min(len(supporting), 3)
+
+        return result
+
+    async def _find_contradictions(
+        self,
+        subject: str,
+        predicate: str,
+        obj: str,
+        project_id: UUID | None = None,
+    ) -> list[FactConflict]:
+        """Find facts that contradict the given fact."""
+        conflicts: list[FactConflict] = []
+
+        # Find opposite predicates
+        opposite_predicates = self._get_opposite_predicates(predicate)
+
+        if not opposite_predicates:
+            return conflicts
+
+        # Search for contradicting facts
+        query = select(FactModel).where(
+            and_(
+                FactModel.subject == subject,
+                FactModel.predicate.in_(opposite_predicates),
+            )
+        )
+
+        if project_id is not None:
+            query = query.where(
+                or_(
+                    FactModel.project_id == project_id,
+                    FactModel.project_id.is_(None),
+                )
+            )
+
+        result = await self._session.execute(query)
+        models = list(result.scalars().all())
+
+        for model in models:
+            conflicts.append(
+                FactConflict(
+                    fact_a_id=model.id,  # type: ignore[arg-type]
+                    fact_b_id=UUID(
+                        "00000000-0000-0000-0000-000000000000"
+                    ),  # Placeholder for new fact
+                    conflict_type="contradiction",
+                    description=(
+                        f"'{subject} {predicate} {obj}' contradicts "
+                        f"'{model.subject} {model.predicate} {model.object}'"
+                    ),
+                    suggested_resolution="Keep fact with higher confidence",
+                )
+            )
+
+        return conflicts
+
+    def _get_opposite_predicates(self, predicate: str) -> list[str]:
+        """Get predicates that are opposite to the given predicate."""
+        opposites: list[str] = []
+
+        for pair in self.CONTRADICTORY_PREDICATES:
+            if predicate in pair:
+                opposites.extend(p for p in pair if p != predicate)
+
+        return opposites
+
+    async def _find_supporting_facts(
+        self,
+        subject: str,
+        predicate: str,
+        project_id: UUID | None = None,
+    ) -> list[Fact]:
+        """Find facts that support the given fact."""
+        # Find facts with same subject and predicate
+        query = (
+            select(FactModel)
+            .where(
+                and_(
+                    FactModel.subject == subject,
+                    FactModel.predicate == predicate,
+                    FactModel.confidence >= 0.5,
+                )
+            )
+            .limit(10)
+        )
+
+        if project_id is not None:
+            query = query.where(
+                or_(
+                    FactModel.project_id == project_id,
+                    FactModel.project_id.is_(None),
+                )
+            )
+
+        result = await self._session.execute(query)
+        models = list(result.scalars().all())
+
+        return [self._model_to_fact(m) for m in models]
+
+    async def find_all_conflicts(
+        self,
+        project_id: UUID | None = None,
+    ) -> list[FactConflict]:
+        """
+        Find all conflicts in the fact base.
+
+        Args:
+            project_id: Optional project scope
+
+        Returns:
+            List of all detected conflicts
+        """
+        conflicts: list[FactConflict] = []
+
+        # Get all facts
+        query = select(FactModel)
+        if project_id is not None:
+            query = query.where(
+                or_(
+                    FactModel.project_id == project_id,
+                    FactModel.project_id.is_(None),
+                )
+            )
+
+        result = await self._session.execute(query)
+        models = list(result.scalars().all())
+
+        # Check each pair for conflicts
+        for i, fact_a in enumerate(models):
+            for fact_b in models[i + 1 :]:
+                conflict = self._check_pair_conflict(fact_a, fact_b)
+                if conflict:
+                    conflicts.append(conflict)
+
+        logger.info(f"Found {len(conflicts)} conflicts in fact base")
+
+        return conflicts
+
+    def _check_pair_conflict(
+        self,
+        fact_a: FactModel,
+        fact_b: FactModel,
+    ) -> FactConflict | None:
+        """Check if two facts conflict."""
+        # Same subject?
+        if fact_a.subject != fact_b.subject:
+            return None
+
+        # Contradictory predicates?
+        opposite = self._get_opposite_predicates(fact_a.predicate)  # type: ignore[arg-type]
+        if fact_b.predicate not in opposite:
+            return None
+
+        return FactConflict(
+            fact_a_id=fact_a.id,  # type: ignore[arg-type]
+            fact_b_id=fact_b.id,  # type: ignore[arg-type]
+            conflict_type="contradiction",
+            description=(
+                f"'{fact_a.subject} {fact_a.predicate} {fact_a.object}' "
+                f"contradicts '{fact_b.subject} {fact_b.predicate} {fact_b.object}'"
+            ),
+            suggested_resolution="Deprecate fact with lower confidence",
+        )
+
+    async def get_fact_reliability_score(
+        self,
+        fact_id: UUID,
+    ) -> float:
+        """
+        Calculate a reliability score for a fact.
+
+        Based on:
+        - Confidence score
+        - Number of reinforcements
+        - Number of supporting facts
+        - Absence of conflicts
+
+        Args:
+            fact_id: Fact to score
+
+        Returns:
+            Reliability score (0.0 to 1.0)
+        """
+        query = select(FactModel).where(FactModel.id == fact_id)
+        result = await self._session.execute(query)
+        model = result.scalar_one_or_none()
+
+        if model is None:
+            return 0.0
+
+        # Base score from confidence - explicitly typed to avoid Column type issues
+        score: float = float(model.confidence)
+
+        # Boost for reinforcements (diminishing returns)
+        reinforcement_boost = min(0.2, float(model.reinforcement_count) * 0.02)
+        score += reinforcement_boost
+
+        # Find supporting facts
+        supporting = await self._find_supporting_facts(
+            subject=model.subject,  # type: ignore[arg-type]
+            predicate=model.predicate,  # type: ignore[arg-type]
+            project_id=model.project_id,  # type: ignore[arg-type]
+        )
+        support_boost = min(0.1, len(supporting) * 0.02)
+        score += support_boost
+
+        # Check for conflicts
+        conflicts = await self._find_contradictions(
+            subject=model.subject,  # type: ignore[arg-type]
+            predicate=model.predicate,  # type: ignore[arg-type]
+            obj=model.object,  # type: ignore[arg-type]
+            project_id=model.project_id,  # type: ignore[arg-type]
+        )
+        conflict_penalty = min(0.3, len(conflicts) * 0.1)
+        score -= conflict_penalty
+
+        # Clamp to valid range
+        return max(0.0, min(1.0, score))
+
+    def _model_to_fact(self, model: FactModel) -> Fact:
+        """Convert SQLAlchemy model to Fact dataclass."""
+        return Fact(
+            id=model.id,  # type: ignore[arg-type]
+            project_id=model.project_id,  # type: ignore[arg-type]
+            subject=model.subject,  # type: ignore[arg-type]
+            predicate=model.predicate,  # type: ignore[arg-type]
+            object=model.object,  # type: ignore[arg-type]
+            confidence=model.confidence,  # type: ignore[arg-type]
+            source_episode_ids=model.source_episode_ids or [],  # type: ignore[arg-type]
+            first_learned=model.first_learned,  # type: ignore[arg-type]
+            last_reinforced=model.last_reinforced,  # type: ignore[arg-type]
+            reinforcement_count=model.reinforcement_count,  # type: ignore[arg-type]
+            embedding=None,
+            created_at=model.created_at,  # type: ignore[arg-type]
+            updated_at=model.updated_at,  # type: ignore[arg-type]
+        )